The Lifelong Learning Iceberg of Information Systems Academics - A Study of On-Going Formal and Informal Learning by Academics
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
This article describes a study that examined the lifelong learning of information systems academics in relation to their normal work. It begins by considering the concept of lifelong learning, its relationship to real-life learning and that lifelong learning should encompass the whole spectrum of formal, non-formal and informal learning. Most world governments had recognised the importance of support for lifelong learning. Borrowing ideas and techniques use by Livingstone in a large-scale 1998 survey of the informal learning activities of Canadian adults, the study reported in this article sought to uncover those aspects of information systems academics’ lifelong learning that might lead policy setters to understand the sources of learning valued by these academics. It could be argued that in the past the university sector was a leader in promoting the lifelong learning of its academic staff, but recent changes in the university environment around the world have moved away from this ideal and academics interviewed from many countries all report rapidly decreasing resources available for academic support. In this environment it is important to determine which learning sources are valued by information systems academic so that informed decisions can be made on support priorities.
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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