Bibliographic record
Abstract
From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.The majority of libraries still provide the majority of their services for free. But fee-based information services attached to non-profit making institutions such as libraries is not a novel concept any longer. The information explosion has brought many changes to, amongst others, the university library and at present industry and commerce rely heavily on the expertise offered by the staff of those libraries.This paper does not propose to address any ideological issues of "feeversus free" but would rather focus on the following: 1 the need for industry and commerce for utilizing the university library and its resources;2 the university library as information broker with reference toinformation skills complemented with specialized subject knowledge and experience 3 the organization of the wide range of services offered and the pricing of these services; and4 a short, but detailed description of INFOBANK, a dynamic andsuccessful regional fee-based information service to industry and commerce at the library of the University of Stellenbosch, South Africa.A university library can therefore be even more effective in promoting access to information by making it available in the community, albeit for a price. Libraries interested in establishing such fee-based services should nevertheless be aware of the pitfalls associated with the creation and management of these services. Knowledge of the latter will undoubtedly help establish a programme that serves the needs of clients whilst contributing towards the overall image of the parent institution.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.131 | 0.020 |
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".