Putting Biologists on the Map: Occupational Osmosis and the Implications of Boundary Crossing
Bibliographic record
Abstract
Occupational groups are frequently required to defend and expand the boundaries of their professions, but they are also increasingly in need of crossing such boundaries in their joint efforts to address environmental and social change. While we possess broad knowledge of the benefits and costs of crossing boundaries, we have limited knowledge of how they add up and the implications of such individual strategies for relations between fields. In this study we ask how actors who strategically cross boundaries moving to and from their professional fields to carry out a professional project, affect inter-field relations. Empirically, we focus on a profession whose members have been required to cross boundaries to achieve their goal, namely Quebec biologists who entered different professional fields to fulfil their mission of preserving the natural environment. Through a qualitative study, drawing on extensive longitudinal archival data spanning over 50 years as well as interviews, we explore how micro-level strategies of boundary crossing affected how biologists connected with other more established fields. Our findings identify three different types of strategic crossing, depending on the context and the phase of field emergence, and document their implications for inter-field relations. We observe how such connections helped biologists solidify and maintain the boundaries of the field as well as provide symbolic and tangible resources to their group through what we call “occupational osmosis”: when crossing boundaries, the connections thus established vascularise the interface between different fields, increase access to resources for emerging fields, and reinforce actor interdependence.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.038 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".