Bibliographic record
Abstract
There is a growing concern throughout higher education that the gap between what university central services traditionally provide and what academics currently need is widening. Members of both the administrative staff and the academic community (staff and students) are finding that the performance of routine tasks is becoming increasingly difficult due to the nature of their institution's information systems. These systems have evolved in an ad hoc basis and are usually comprised of multiple unconnected data repositories. Users are often prevented from carrying out work by inappropriate access control mechanisms and the lack of appropriate client software. There are broadly two approaches to addressing this problem. One is the "big bang", where all existing systems are replaced simultaneously with a new single centralised system. Before such an approach can be taken it is necessary to fully understand the dynamics of an institutions information systems, in order to specify the new, all encompassing system. This is a major task in itself. An alternative, more attractive, approach is to integrate existing systems using user-centric portals. This is the objective of the INSIDE project, which is piloting value-added services based on distributed information bases, in order to further the development and delivery of a "joined up system" for an institution.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".