Unlevel Playing Fields: Gender and its Role in the Early Stages of Institutional Fields
Bibliographic record
Abstract
Institutional studies on fields have been neglectful of gender. To extend the boundaries of current understanding of institutional fields, we develop a theory by drawing insights from doing gender and gender-status beliefs theories and by considering fields as relational spaces within which diverse actors interact with one another on particular issues. We argue that gender plays a key role in the emerging fields in three ways; (1) issues themselves are often gender-marked, (2) issue-leading roles such as organizers of field configuring events are gendered, and (3) gender composition of field participants influences the development of new fields into the next stages. In considering the gendered nature of issues, issue-leading roles, and gender makeup of field participants, we introduce a new model of the early stage of fields to explain gendered dynamics that structure interactions and shape the development of new fields. With our model, we advance the existing literature on early stages of institutional fields.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".