Bibliographic record
Abstract
How to Win Friends and Influence People Janna Klostermann (bio) There was something aboutthe way my yoga-loving,jog-on-your-lunch-break,public-servant friend Lauriespotted me plowing through amaple cream donut and cheered, Aw, honey, good for you!There was something aboutthe way Mrs. Miracle Network TVJoyce Meyer heralded, No scripting here; it's all done by the Holy Spirit.There was something aboutthe way the captain of thePeterborough Petes whispered,after downing fourteen cansof cold-enough Coors Light, We don't have to have sex, but would you at least give me a blow job?There was something aboutthe way Real Housewife ofBeverly Hills Lisa Vanderpumpbatted her eyelashes andannounced, unscripted, [End Page 281] I wouldn't let any of my waitresses burn to death.There was something aboutthe way my low budget,"pay what you can" therapistrolled her eyes and proclaimed, Don't bother critiquing the system if you can't even meet your own needs.There was something aboutit all that made me realizeif I truly wanted power—power where there isutterly none to be had—I'd either have to startspeaking for the Holy Spiritand soliciting blow jobsor burning to death. [End Page 282] Janna Klostermann Janna Klostermann is a PhD student in Carleton University's Department of Sociology and Anthropology in Ottawa, Canada. She is researching the classed politics of everyday work, and is writing a collection of queer stories about care. Her work has appeared in Canadian Woman Studies, Literacy and Numeracy Studies, Our Times, Reading Sociology, Room, and Women & Environments. She can be reached at jannaklostermann@gmail.com. Copyright © 2018 Janna Klostermann
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.104 | 0.072 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".