Using Causal Layered Analysis to Explore the Relationship between Academics and Administrators in Universities
Bibliographic record
Abstract
Universities are complex organisations requiring a range of skills, knowledge and expertise to operate effectively. Since the last quarter of the 20th century, when a separate administrative work jurisdiction began to emerge, academics and administrators have had to co-exist in universities. With growing pressures from government for accountability and transparency during that time, that coexistence has been increasingly characterised by a tension most often described as a ‘divide’. This paper reports on the findings of a research project undertaken in 2008 using Causal Layered Analysis to explore the nature of this tension, and perceptions that a ‘divide ’ exists between academic and administrative staff in universities. higher education management, universities, academics, administrators, professional managers “We often live in two different worlds. The academics feel that the administrator’s main drive in life is to push as much annoying paperwork as possible on to the academics…They do not feel that anything the administrators do is worthwhile for the student or them. The administrators feel that the academics are so removed from ‘real life ’ that there’s no point in trying to explain ‘logic ’ to them.”
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".