The Copyright Librarian: A Study of Advertising Trends for the Period 2006–2013
Bibliographic record
Abstract
Academic libraries are creating copyright positions to respond to the evolving and litigious copyright environment associated with digital content and services. This paper reports on a study of advertisement trends for copyright positions available in academic libraries. A content analysis of job advertisement data was carried out on data generated from JobLIST, an electronic listserv of the American Library Association (ALA) where library positions from the United States and Canada are posted. Job data were generated by searching the JobLIST database using the following search terms: copyright, intellectual property, scholarly communication, repository, electronic resources, licensing, and digital. Data were for the period August 2006 through April 2013. The search generated 2799 job advertisements (ads) of which 264 jobs mention ‘copyright’ in the title or text of the job advertisement (job ads). Of the 264, none required a Juris Doctor (JD) although 5 preferred a JD. The MLS/MLIS was always mentioned first. Of the 264 jobs, 16 were copyright officer/manager type positions. Between 2006 and 2011, there was a slight but steady growth in the positions mentioning copyright from 9% (2006) to 13% (2011). In the first quarter of 2013, copyright positions already represented 8% of the positions retrieved from JobLIST. The majority of the positions were a combination of copyright and related areas like intellectual property, scholarly communication, electronic resources, licensing and digital management. It is evident from the data that the copyright librarian or competence in copyright is a prerequisite for current and future needs of academic libraries and academic institutions in general.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".