Hey y'all! So I know I haven't posted much lately BUT college has been crazy. And related to that, for one of my classes I'm doing a study on the relationship between time within the greater fandom community and understanding of fanfiction-related jargon. Below I have the link to the survey which will just have three multiple choice questions relating to your relationship to fandom and then a list of 15 terms to define if you know them.Â
Please reblog this or otherwise share the survey link with anyone you think might be interested so I can have as large of a sample size as possible for this. The form will be open until April 24th.Â
Thank you all so much!Â
(and yes, I will post the data when I get it all collected for all you fellow nerds)
A survey to determine the relationship between the understanding of fandom jargon and the time one has been in the fandom community
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Playing Homestuck police is good for a laugh, but at some point we've got to acknowledge that it's a sufficiently influential piece of media that some portions of its distinctive jargon have in fact entered the broader vernacular. There are people out there who refer to visual novel style dialogue portraits as "talksprites" who've never heard of Vriska Serket.
Love of corporate bullshit is correlated with bad judgment
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
I'm a writer, so of course I care about words! But I'm a writer, so I also think that words are improved by their malleability, duality and nuance.
This is one of the things I love about being a native English speaker â this glorious mongrel language of ours is full of extremely weird words, like "cleave," which means its own opposite ("to join together" and "to cut apart"). English is full of these words that mean their own opposite, from "dust" to "oversight" to "weather":
This is what you get when you let a language run wild, with meaning determined (and contested) by speakers. Not for nothing, my second language is Yiddish, another glorious higgeldy-piggeldy of a tongue with no authoritative oversight and innumerable dialects.
Semantic drift is a feature, not a bug. It's how we get new words, and new meanings for old words. I love semantic drift! I mean, I'd better, since, having coined "enshittification," I'm now destined to have a poop emoji on my headstone. Having coined a word â and having proposed a precise technical meaning for it â I am baffled by people who make it their business to scold others for using enshittification "incorrectly." "Enshittification" is less than five years old, and we know when and how it was invented. If you like it when I make up a word, you can't categorically object to other people making up new meanings for this word. I didn't need a word-coining license to come up with enshittification, and you don't need a semantic drift license to use it to mean something else.
I wrote a whole danged essay about this, but still, hardly a day goes by without someone trying to enlist me in their project to scold and shame strangers for using the word incorrectly:
The fact that a neologism is sometimes decoupled from its theoretical underpinnings and is used colloquially is a feature, not a bug. Many people apply the term "enshittification" very loosely indeed, to mean "something that is bad," without bothering to learn â or apply â the theoretical framework. This is good. This is what it means for a term to enter the lexicon: it takes on a life of its own. If 10,000,000 people use "enshittification" loosely and inspire 10% of their number to look up the longer, more theoretical work I've done on it, that is one million normies who have been sucked into a discourse that used to live exclusively in the world of the most wonkish and obscure practitioners. The only way to maintain a precise, theoretically grounded use of a term is to confine its usage to a small group of largely irrelevant insiders. Policing the use of "enshittification" is worse than a self-limiting move â it would be a self-inflicted wound.
Colloquialization doesn't dilute language, it thickens it. Using a powerful word to describe something else can be glorious. It's allusion, metaphor, simile. It's poesie. It's fine. Bemoaning the "tsunami" of bad news doesn't cheapen the deaths of people who die in real tsunamis. Saying that the Trump administration "nuked" the Consumer Finance Protection Bureau doesn't desecrate the dead of Hiroshima and Nagasaki. Calling creeping authoritarianism a "cancer" doesn't denigrate the suffering of people who have actual cancer.
What's more, devoting your energies to "correcting" other people's allusive language makes you a boring, tedious person. Sure, you can have a conversation with a comrade about making inclusive word choices, but interrupting a substantive debate to have that discussion is unserious. The words people use matter (I care a lot about words!) but they matter less than the things people mean. Keep your eye on the prize (metaphorically) (for avoidance of doubt, there is no prize) (both the prize and the eye are metaphors).
(By all means, get angry at people who intentionally use slurs. None of this is to say that you should tolerate â or be subjected to â language that is intended to dehumanize you.)
It's time we admitted that it's no good replacing an offensive term with a phrase that no one understands. Calling it "child sexual abuse material" is a good idea, but no one actually calls it that. The customary phrase is actually "child sexual abuse material, which most people call 'child porn,' but which we should really call 'child sex abuse material.'" If your goal is to avoid saying "child porn" (a laudable goal!), this isn't achieving it.
None of this means that I am immune to being rubbed up the wrong way by other people's language choices. Having been mentored by the science fiction great Damon Knight, I have been infected by many of his linguistic peccadillos, which means that if you say "out loud" in my earshot, I will (mentally) "correct" it to "aloud" (yes, "out loud" is fine, but Damon had a thing about it and it got stuck in my brain).
I am especially perturbed by "business English," the language of the commercial class, their cheerleaders in the press, and (alas) many of their critics. Anytime someone refers to a sector as a "space" (as in "I'm really getting into the AI space") it's like they're making me chew tinfoil. Superlatives like "thought-leader" are so self-parodying I have to check every time someone utters one aloud (see?) to verify that they're not being sarcastic. Objects of derision should be referred to by their surnames, not their given names ("Musk" is vituperative, "Elon" is friendly â though, thanks to the glorious and thickening contradictions of language, calling someone by their surname can also be affectionate). I steer clear of jargon used by firms to lionize themselves, like "hyperscaler."
I share the impulse to impose my linguistic preferences on the people around me. I just (mostly) suppress that impulse and try to focus on substance rather than style, at least when I'm trying to understand others and be understood by them. But yes, I do silently judge the people around me for their word choices â all the time.
That's why I immediately pounced on "The Corporate Bullshit Receptivity Scale: Development, validation, and associations with workplace outcomes," an open access paper in the Feb 2026 edition of Personality and Individual Differences by Shane Littrell, a linguistics postdoc at Cornell:
Littrell set out to evaluate "corporate bullshit," a linguistic category that is separate from mere "jargon." Jargon, Littrell writes, is a professional vocabulary that serves a useful purpose: "facilitat[ing] communication and social bonding, increas[ing] fluency, and help[ing] reinforce a shared identity among in-group members."
Bullshit, meanwhile, is "semantically, logically, or epistemically dubious information that is misleadingly impressive, important, informative, or otherwise engaging." There's a whole field of bullshit studies, with investigations into such exciting topics as "pseudo-profound bullshit" (think: Deepak Chopra).
Littrell borrows from that field and others to investigate corporate bullshit, formulating a measurement index he calls the "Corporate Bullshit Receptivity Scale." In a series of three experiments, Littrell sets out to determine who is the most susceptible to corporate bullshit, and what the correlates of that receptivity are.
Littrell concludes that corporate bullshitters themselves are pretty good at identifying bullshit (they have a high "Organizational Bullshit Perception Score"). In other words, bullshitters know that they're bullshitting. When a corporate leader declares that:
This synergistic look at our thought leadership will ensure that we are decontenting and avoiding reputational deficits with our key takeaways as effectively as we can in order to sunset our resonating focus.
they know it's nonsense.
This reminded me of the idea that cult leaders tell obvious lies to their followers as a way of forcing them to demonstrate their subservience. When Trump demands that his followers wear clown shoes:
He's engaging in a dominance play that forces his feuding princelings and their lickspittles to humiliate themselves and reaffirm his supremacy.
There are plenty of rank-and-file workers inside corporations who have high OBPSes and know when they're being bullshitted, but Littrell also identifies a large cohort of low-OBPS workers who are absolutely taken in by corporate bullshit.
Here we get to a fascinating dichotomy. Both the low-OBPS and high-OBPS workers can be described as being "open minded," but "open" has a very different meaning for each group. Workers who are good at spotting bullshit score high on open-mindedness metrics like "willingness to engage" and "willingness to reflect," both characteristic of the "fluid intelligence" that makes workers good at solving problems and doing a good job.
Meanwhile, workers who are taken in by bullshit are "open minded" in the sense that they are bad at analytical reasoning and thus easily convinced. These people test poorly on metrics like "logical reasoning" and "decision-making," and score high on "overconfidence in one's intellectual and analytic abilities." They are apt to make blunders that "expose organizations to financial, reputational, or legal risks."
But they're also exactly the workers who score high on "job satisfaction," "trust in one's supervisor," and "degree to which they are inspired by corporate mission statements." These people are so open minded that their brains start to leak out of their ears. Or, as Carly Page put it in The Register: "jargon sticks around not just because executives enjoy using it, but because many people respond to it as if it were genuine insight":
This creates a feedback loop where bosses get rewarded for using empty and maddening phrases, and their workforce gets progressively more skewed towards people who are bad at spotting bullshit and at exercising their judgment on the job. It's quite a neat â and ugly â explanation of why bullshit proliferates within organizations, and how organizations come to be completely overrun with bullshit.
This is a fascinating research paper, and while I've focused on its conclusions, I really suggest going and reading about the methodology, especially the tables of "corporate bullshit" phrases they generated for their experiments (Tables 1, 2 and 3). This is some eldritch horror bullshit:
By solving the pain point of customers with our conversations, we will ideate a renewed level of end-state vision and growth-mindset in the market
between us and others who are architecting to download on a similar balanced scorecard.
What's more, these are all based on real examples of corporate bullshit from leaders at large corporations, with a few words rotated to synonyms drawn from the business-press.
I'm a writer. I really do care about language. Sure, I get frustrated with scolds who rail against semantic drift or engage in petty, pedantic corrections, but not because words don't matter. They matter, a lot. But language isn't math (which is why double negatives are intensifiers, not negators). It can obscure (as with bullshit) or it can enlighten (as with poesie) or it can enable precision (as with jargon). Arguments about language matter, but what matters about them isn't subjective aesthetics, nor is it a peevish obsession with "correctness." What matters is the way that language operates on the world (and vice versa).
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Mistress Aspyce: I'm working, doll. This better be about work.
Me: It is about work! One of the ponyboys found a "Human to LinkedIn Post" translator, and it's amazing. Check it out!
RyePony's Original: "Sometimes I wear a latex catsuit under my clothes during Teams calls."
Translation: "I'm a big believer in bringing your whole self to work and finding unique ways to stay energized during back-to-back Teams calls. It's all about that hidden layer of personal empowerment that drives peak performance. #Authenticity #WorkLifeIntegration #PersonalGrowth"
Me: We have to try it!
Original: "I'm a sissy maid dressed fully in latex who cleans for my mistress."
Translation: "I am a dedicated domestic service professional specializing in high-end maintenance in specialized attire, committed to delivering exceptional results for my primary stakeholder."
Original: "I take pretty boys and break them into my personal rubberdolls, who beg to suck on my strap-on."
Translation: "I'm passionate about identifying high-potential talent and transforming them into dedicated, specialized assets within my personal ecosystem, where they'll be eager to engage with my leadership tools. #TalentAcquisition #Mentorship #GrowthMindset."
Me: "Leadership tool" is my new favorite euphemism for a strap-on.
Original: "Worthless slave! Thank me for the chance to feel my whip!"
Translation: "Team members are grateful for this high-pressure growth opportunity. #Grindset"
Original: "This used sissy slut has had the gag reflex fucked right out of her."
Translation: "This experienced individual has developed exceptional oral presentation skills through hands-on client engagement."
Mistress Aspyce: I can't tell if this is an improvement from what I usually get in my DMs.
Original: "A tiny cock like yours belongs locked away in a chastity cage."
Translation: "I'm thrilled to announce a new strategic approach to resource management: Your underperforming assets are being transitioned to a secure, long-term containment solution to ensure maximum operational focus."
Me: Can you imagine people who actually talk like this?
Mistress Aspyce: (Slowly turns to look at the Outlook calendar open on her laptop and develops a thousand-yard stare.)
Me: Mistress? Are you okay, Mistress? Let me fetch the Cognac. It looks like you could use a drink.
A/B Testing
AGI (Artificial General Intelligence)
AGI Acceleration
AI Accelerators
AI Affordances
AI Cognitive Pattern
AI Cognitive Spirit
AI Command Palette
AI Companion
AI Copiloting
AI Feature Design
AI Governance
AI Leading States
AI Literacy
AI Models
AI Partner
AI Product Design
AI Product Management
AI Prompting
AI Safety
AI Strategy
AI Suggestions Patterns
AI Watermarking
AI Wireframing
AI as Assistant
AI as Collaborator
AI as Creative Partner
AI as Infrastructure
AI as Medium
AI as Mirror
AI as Substrate
AI as Tool
AI as Toy
AI as Utility
AI-Augmented Design
AI-Generated Content Detection
AI-Native Design
AI-Powered Search
API (Application Programming Interface)
ASI (Artificial Superintelligence)
Accepted/Reject Flow
Adaptive UI
Adversarial Examples
Agent
Agent Builders
Agents Loop
Alignment
Ambient AI
Appropriateness Reliance
Assistance
Automation
Automation Spectrum
Autonomous Agent
Autonomous Vehicle
Autopilot Mode
BMOA (Biggest Method of AI-Driven Development)
Bias & Fairness
Black Box
Browser Use
C2PA (Coalition for Content Provenance and Authenticity)
CV (Computer Vision)
Capability Elicitation
Career Modalities
Chain of Thought
Client AI
Cloud AI
Cognitive Load
Cognitive Offloading
Collaboration
Compute Use
Computer Use
Conscience
Consent
Considerate Display
Content Models
Content Moderation
Context
Control
Copilot Mode
DL (deep learning)
DL Engines
Data Labeling
Data Poisoning
Data Privacy
Dataset Bias
Dataset Curation
Design Automation
Design Education
Design for AI
Design for AI/AGI
Digital Provenance
Digital Twin
EUI/AI
Embedded AI
Embodied AI
Emergent Capabilities
Empathy with AI
Ethics
Evaluation
Explainable AI
Fairness Metrics
Fake News
Few-Shot Prompting
Fine-Tuning
Foundation Model
Free Speech
GOFAI (Good Old-Fashioned AI)
GenAI Interns
Generative AI
Generative Design
Grounding
Hallucination
Harness
Human in the Loop
Human-Centered AI
Human-on-the-Loop
Image Generation
Image-to-Image
Image-to-Text
Inference Efficiency
Inference Engine
Intent Classification
Intent Detection
Interface
JSON Mode
Justifiable Risk
LLM (Large Language Model)
LLMOps (Large Language Model Operations)
LLMs (Large Language Models)
Latency of Computation
Latency of Response
Meta-Prompt
Meta-Prompting
Model Drift
Model Hallucination
Model Misuse
Model Poisoning
Model Training
Model Use
Multi-modal
Multi-modal Interface
NLP (Natural Language Processing)
NSAI (Neural Symbolic AI)
NSFW Filter
Open Source
Open Source AI
PEFT (Parameter Efficient Fine-Tuning)
Personalization
Personalized AI
Plan Mode
Plans/Planning
Post-Training
Pre-Training
Predictive UI
Proactive AI
Proactive AI DESIGN
Progress Disclosure
Prompt
Prompt Chaining
Prompt Debugging
Prompt Design
Prompt Engineering
Prompt Evaluation
Prompt Injection
Prompt Injection Mitigation
Prompt Libraries
Prompt Literacy
Prompt Template
Prompt Versioning
Prompting
Push vs Pull
RAI (Responsible AI)
RAS (Retrieval Augmented Generation)
RLF
RLHF (Reinforcement Learning from Human Feedback)
Recommendation Engine
Reinforced Learning
Reinforcement Learning
Response/AI
Roles & Tone
Rules
Safety Filters
Semantic Search
Shadows Mode
Silicon Use
Speculative Design for AI
Speech
Stochastic Prompt
Streaming Text Effect
Structure
Subagents
Subtasks
Supervised Learning
Supervision & Oversight
Symbolic AI
Synthetic Data
Synthetic Users
System Prompt
Task Delegation
Taxonomy of Agents
Temperature
Text-to-3D
Text-to-Code
Text-to-Image
Text-to-Speech
Text-to-Video
Throughput
Tokens
Tool Use
Top-k Sampling
Toxicity Detection
Training
Transfer Learning
Transformer
Transparency
Trust
Trust Calibration
Unlabeled/Raw AI
Unsupervised Learning
Usability
Vector Search
Voice
Voice Interface
Voice Language Model
Voice Recognition
Weights
Workflow Automation
Workflows
Zero-Shot Prompting
