Nexus: An investigation into the library and information services workforce in Australia. Final report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An analysis of the research data collected in the neXus census with a particular focus on the findings relevant to the States and Territories and the various library sectors in Australia. In recent years, there has been considerable anecdotal evidence concerning challenges facing the profession, notably about people leaving the library employment and the ‘greying’ of the profession. Through the neXus census, the researchers have captured accurate data about the LIS workforce, both currently employed and retired, recent graduates embarking on library careers and students enrolled in LIS studies. The report provides a demographic, educational and employment picture of the Australian library and information profession, as well as identifying diverse workforce planning activities being undertaken in the sector. The research is aligned with similar projects completed in Canada, the United Kingdom and United States, enabling a comparison between the situations in Australia and other countries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it