Bibliographic record
Abstract
Mikroaggressioner som begreb blev første gang beskrevet af psykiater Chester Pierce (Pierce 1970), som brugte det specifikt til at beskrive racerelaterede verbale angreb på afroamerikanere. Siden da er begrebet blevet bredt mere ud, og det er nu en samlebetegnelse for negativ kommunikativ adfærd. Mikroaggressioner er en underkategori til den bredere gruppe af psykologiske aggressioner. Denne kategori anvendes til at beskrive mobning, chikane, uforskammet adfærd og verbale aggressioner (Schat m.fl. 2006). Begrebet psykologisk aggression bliver typisk anvendt, når der er tale om ikke-fysisk vold, omend de to fænomener ofte følges ad. Der findes talrige empiriske studier, bl.a. fra Norge, Canada og Australien (Einarsen & Raknes 1997; Pizzino 2000; Taylor & McLoughlin 2017), der viser, at psykisk vold er et udbredt fænomen, især på arbejdspladser.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".