Qualitative Research Using Numbers: An Approach Developed in France and Used to Transform Work in North America
Bibliographic record
Abstract
Qualitative research is often opposed to quantitative research. But numbers can play an important role in illustrating analyses in qualitative research. Their persuasive, concrete nature can help ensure the success of a workplace intervention, especially in the North American context, where numbers are treated very seriously. We describe a method of work analysis and transformation developed at the Conservatoire national des arts et métiers in Paris, where the meaning of the numbers used is critical. We think that the numbers used in work analysis have a different meaning from that in a "pure" quantitative study, where they are submitted to statistical procedures for hypothesis testing. Using examples from recent studies carried out in Québec and Canada in collaboration with unions or joint health and safety committees, we show that counting can be part of qualitative analysis, enrich our portrait of organizational and physical aspects of the work process, and help indicate pathways for workplace improvement.
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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 | 0.143 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".