System development conflict during the use of an information systems prototyping method of action research
Bibliographic record
Abstract
In one particular action research (AR) methodology, information systems prototyping (ISP), the goals are to involve the researcher in a facilitative and collaborative role with stakeholders in the development of an information system that satisfies their collective needs. But what happens when political and structural conflict and coercive action erupts? This article features an AR case, where the development of an electronic patient record in a heart clinic, resulted in a period of intense structural conflict, and the dismissal of an organizational member. Further analysis suggests that four factors can explain these unusual outcomes and their relationship with the use of an ISP method. These include: the specification of measures and perceptions of success within the AR method (goals); general problems with the AR methodology and/or its clear delineation (processes); problems in using a particular AR methodology in a specific time and place (contingency); and problems with the researcher’s implementation of the AR processes (implementation). The study also highlights a number of areas for development of ISP.
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 | 0.362 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".