Exclusive versus everyday forms of professional knowledge: legitimacy claims in conventional and alternative medicine
Bibliographic record
Abstract
In this paper I present a model of professional knowledge forms that accounts for the different, and sometimes contradictory, ways in which medical doctors (MDs) and various complementary and alternative medicine (CAM) practitioners define their competencies and make legitimacy claims. The first section provides a schema for problematising knowledge and its relationship to legitimacy by distinguishing between the context, form and content of professional knowledge. I draw particularly upon Jamous and Peloille's (1970) distinction between the technical or indeterminate forms of professional knowledge. I argue that their characterisation might be enriched by attending to dimensions of 'exclusive' versus 'everyday' knowledge forms. In particular, I point out that both technical and indeterminate forms are amenable to exclusion, or conversely can be made accessible as everyday knowledge. Both forms can thus be employed in attempts to legitimate professional practice. In the final section, I map the current context of CAM and biomedicine onto this expanded professional knowledge map.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.115 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".