Bibliographic record
Abstract
My research examines how different organizational phenomena function under psychological and cognitive constraints. My first study examines how audiences evaluate an established or taken-for-granted category in negative moods. Categories facilitate exchange by serving as mental models or schemas that substitute for an organization’s attributes to help audiences make sense of what they see. Established categories are further postulated to be legitimized and taken for granted by audiences. Both organizations and audiences are thought to place a high value on category membership, preferring the schema-based category to the individual attributes underlying the category. Considering the preferences of a broad audience segment about an established category, I examine the boundary conditions that can cause the schemas of a legitimized category to fail. I propose that negative mood or affect will blur the category boundary causing it to no longer be preferred to the individual attributes. I further hypothesize that negative affect will induce a reversal of preferences, and offer a unified theory as to why negative affect can cause audiences to prefer the attributes underlying the category over the category itself in their evaluations. Results from data on a representative sample of individuals support these hypotheses. In my second study, I examine how social capital accrues to individuals who were part of a group from which a member achieved prominence only after the dismantlement of the group. I employ a difference-in-differences estimation strategy to identify endogenous social effects in the context of the Hollywood film industry and find significant positive results for egos who worked with ex-post Oscar winning alters within four to six years prior to the alters’ Oscar win. Social capital effects break down, however, for length of prior years in either the too recent or too distant past. I attribute these findings to individuals’ incorrect recall of past events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.120 | 0.033 |
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".