Institutional Bases of Identity Construction and Reproduction: The Case of Underground Coal Mining
Bibliographic record
Abstract
In this article I use institutional theory to illustrate the process of identity formation and reproduction in the context of a male‐dominated work environment. Based on a case study of an underground colliery in Nova Scotia, Canada, I will illustrate the functioning of powerful institutions in two distinct senses. First, social obligations rooted in a ‘logic of appropriateness’ (March and Olsen, 1989) dictated appropriate behaviours for miners in their roles as underground miners and patriarchs. Second, internalized understandings of reality rooted in a ‘logic of orthodoxy’ (Scott, 1995) formed a set of constitutive rules to which miners adhered because it was inconceivable to do otherwise. I use a microinstitutional perspective (Zucker, 1991) that highlights the constraints embedded in many work contexts that serve to tacitly yet powerfully regularize behaviours in problematic ways. Social and historical conditions are therefore incorporated into this analysis as constitutive forces that are a product of human action. This manner of theorizing gender is consistent with Connell’s (1987) ‘theory of practice’ that seeks to understand social structure by focusing on what people actually do, the way human agency shapes history, and how practice itself is necessary for institutions to maintain their hegemony and resist change.
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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 | 0.005 | 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 | 0.021 | 0.046 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".