Bibliographic record
Abstract
Purpose – The purpose of this paper is to propose both short-term and long-term recommendations, with the potential to help cultural information systems (IS) research overcome the definitional and epistemological problems that cause it to remain largely immature. Design/methodology/approach – The paper uses an extensive literature review to identify the major definitional and epistemological problems inherent in cultural IS research and to propose ways to overcome these problems. Findings – The paper finds that cultural research in the area of IT and people needs to employ more consistent definitions of the culture construct and that such research could benefit from a diversification of the epistemological approaches employed. Originality/value – The present paper finds that a more contemporary definition of culture is needed alongside a greater emphasis of the interpretivist approach to move cultural IS research toward maturity. The paper also suggests that anthropology constitutes a promising reference discipline for cultural IS research. In line with recent research in IS and anthropology, future IS research may consider defining culture consistently as shared values among the members of a collective rather than as a nation state since the former definition accounts for the fact that nation states are no longer culturally homogeneous.
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 | 0.056 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.019 | 0.041 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".