Reading between the lines: gender, work and history
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the role of history in the creation of gender dynamics at work. Design/methodology/approach Drawing on an ANTi‐history – which draws on actor‐network theory (ANT) – and critical sensemaking framework, the authors analyze a written history of a teachers' union to examine how historically contextualized networks of actors shape notions of gender. Findings The findings support the notion of history as socially constructed story telling, which serves to shape rather than describe gendered relations at work. Research limitations/implications The research is limited to archival materials as the participants are not available as direct informants. Archives by their nature are incomplete and some accounts are summaries. Practical implications Understanding the socially constructed role of history will help management educators and practitioners to examine historical accounts as part of the problem of gendered relations. The paper reinforces the notion that understanding of discrimination may be lost as power imbalances are written out of historical accounts in the attempt to be politically correct. Originality/value The paper's contribution to research lies in its application of new methods of historical analysis (namely, ANTi‐history and critical sensemaking) and a focus on history as a powerful sensemaking device that shapes on‐going sensemaking.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".