pERFORMANCE APPRAISAL TOUCHES ON
Bibliographic record
Abstract
one of the most emotionally charged ac~ tivities in business life-the assessment of a person's contribution and ability. This is true whether the business is that of running a university library or oper-ating a commercial organization. In the spring of 1971, a study was un-dertaken by the author to compile in-formation on performance appraisals of libr.arians in college and university libraries. The objectives of the study were to: ( 1) determine approaches used in ap-praising librarians, together with the apparent success, or lack of success; of these approaches; ( 2) compare the results with per-formance appraisal concepts ex-pressed in recent literature; ( 3) draw conclusions:which could be helpful to those responsible for appraisal of professional person-nel in libraries. All university libraries in the United States and Canada having more than fif-' teen librarians on the staff were · sur-veyed. Out of this total of 185 libraries, responses were received from 138. The majority, almost 95 percent, indicated that some form of appraisal was used. Ttte methods ranged from.a casual ob-servation of staff members by the direc-tor with no written record made, to lengthy interviews with staff members Ms. Johnson is cataloger at the Memphis Public Library and Information Center,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".