Heterarchical social organizations and relational models: Understanding gender biases in psychological science
Bibliographic record
Abstract
In the present study, we sought to explain changes in the proportion of men and women working within North American psychological science in terms of a heterarchical social organization defined by norms and conventions of society, the structure of higher-education institutions, as well as scientific communities. Using archival records from psychology within the U.S., we found that the demographic shift from male-dominated to female-dominated reflects an asymptotic relationship that has been established in the last two decades. An examination of three potential indicators of status (PhD department appointments, general science awards, and scientific awards in psychology) did not indicate a similar trend compared to the ascension of women within psychological science. We believe that this reflects a heterarchical structure: disparate criteria were used to assign women’s status in the social networks of academic institutions and scientific research. Moreover, we also claim that the increase in the number of women and “female-associated” topics has resulted in a general change in the status of psychology.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".