Location, Location, Location: The Impact of Organisational Structure on Library and Information Studies Programmes
Bibliographic record
Abstract
As a discipline, library and information studies (LIS) is often considered to lack visibility and a clear identity within academia. Poor understanding of the nature of our field/discipline and our relatively small size has led to LIS programmes being partnered with a range of other subjects, located within diverse faculty structures. We suggest that this can impact on the development of both LIS curricula and research as LIS academics are brought into interdisciplinary relationships with school and faculty colleagues. The study reported here analysed the location of a sample of LIS programmes from New Zealand, Australia, the United States of America, Canada, the United Kingdom, South Africa and Singapore. Compared with previous studies, we found a higher number of ‘stand-alone’ schools as well as some national differences. We reflect on our experiences in a Business School, partnered with the information systems discipline, noting some key differences in boundary setting, field configuration, the use of theory in our research and links with practitioner communities. We conclude that there is a vicious circle in that the LIS discipline’s lack of clear identity leads to it being partnered with disparate other fields which, in turn, further weakens its identity.
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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".