Meta-Synthesizing Qualitative Research in Information Systems
Bibliographic record
Abstract
The beauty of qualitative research is in its appreciation of context, pluralism and diversity. However, this appreciation creates a problem; the results from such studies are often dissonant or appear to be disconnected. On the other hand, there is a growing acceptance and appeal for the rich insights gained from qualitative studies in Information Systems. In this paper, we propose the Qualitative Meta-Synthesis as a credible method to create substantive Information Systems theories from qualitative studies. We reflect on how Qualitative Meta-Synthesis has been used in other fields before proposing a set of guidelines. The paper makes a contribution to practice and theory. To theory, the paper offers emergent fields in Information Systems, especially those that depend a great deal on qualitative research (such as community informatics, e-government and ICT for development) a tool with which to create micro-, meso- and macro- level theories. For practice, the paper offers an approach that could assist policy makers to make sense of the dissonant findings from qualitative studies towards the creation of policy.
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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 | 0.294 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".