Working in the lab : social organization of research and training in biomedical research labs in Canada and its relationship to research funding
Bibliographic record
Abstract
There are significant indications that a transformation has occurred in the organization of research and post-graduate training in the biomedical sciences in Canada over the last few decades. A typical academic lab in the 1960s was small: a professor, maybe a technician, and perhaps a graduate student or two. However, many labs now have twenty or more members, most of which are graduate students and post-doctoral researchers. Changes in social organization of academic labs during this period and the reasons they occurred have not been systematically studied. Nor has the relationship between the social organization of research and research funding, despite the dependence of most academic scientists in North America on external funding. In my dissertation, findings showed that the social organization of biomedical labs in leading universities in Canada has been transformed over the last few decades. Data gathered from more than 70 in-depth work history interviews done in the context of an ethnographic study of biomedical research labs at two leading Canadian research universities in 2002-2003 suggests that there have been changes in the social organization of work (e.g. division of labour, recruitment, structure of organization and occupation) in the biomedical sciences in these universities since the 1960s, and that these changes, including the emergence of larger labs, were due primarily to effects resulting from an increase in competition for federal grants in the 1980s, after institutional accommodation of external funding made biomedical faculty dependent on them. The main argument is that the major influence has been dynamic federal research funding and its institutional accommodation. Specifically, dependence of an academic career on maintaining competitive federal funding led scientists to change their work and organizing practices; applying for multiple grants, and recruiting graduate students and postdocs instead of technicians as in the past, which led to a transformation in both the organization of research and training. Current dependence of biomedical scientists supported by standard federal grants (as most are) on the work of trainees in a competitive funding environment is associated with the incorporation of trainees into the production of faculty research and publication with several institutionalized practices, using a change in the reward system in science. The career strategy necessary, obtaining multiple grants, also means that individual labs of successful biomedical scientists grow in size. As they do, scientists tend to change their practices, such that the social organization of research and post-graduate training in large and small labs typically differs considerably.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".