Only for “purely scientific” institutions: the Medical Library Association's Exchange, 1898–1950s
Bibliographic record
Abstract
OBJECTIVE: Centralized exchanges of scientific materials existed by the late nineteenth century, but they did not include medical publications. North American medical leaders therefore formed an association of institutions to run their own exchange: the Medical Library Association (MLA). After providing background to the exchange concept and the importance of institutional members for MLA, this article examines archival MLA correspondence to consider the role of its Exchange in the association's professional development before the 1950s. RESULTS: MLA's membership policy admitted only libraries open to the medical profession with a large number of volumes. But the correspondence of the MLA Executive Committee reveals that the committee constantly adjusted the definition of library membership: personal, public, sectarian, commercial, allied science, and the then-termed "colored" medical school libraries all were denied membership. CONCLUSION: Study of these decisions, using commercial and sectarian libraries as a focus, uncovers the primary justification for membership exclusions: a goal of operating a scientific exchange. Also, it shows that in this way, MLA shadowed policies and actions of the American Medical Association. Finally, the study suggests that the medical profession enforced its policies of exclusion through MLA, despite a proclaimed altruistic sharing of medical literature.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".