Investigating Information Systems with Positivist Case Research
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 paper offers a rigorous step-by-step methodology for developing theories and contains specific and detailed guidelines for IS researchers to follow in carrying out positivist case studies. The methodology is largely inspired by the work of Yin [2003], Eisenhardt [1989], Miles and Huberman [1994] and several others who are strong proponents of and have wide experience in this research approach. It also relies on previous key contributions to the positivist case research method in IS [Benbasat et al., 1987; Lee, 1989; Dubé and Paré, 2003]. We illustrate how this methodology can be applied in our field to help find new perspectives and empirical insights. In addition, the desired qualities associated with several of the proposed concepts and the techniques and tools included in the methodology are presented. We believe that the two detailed case studies presented in this paper represent highly rigorous, yet different applications of the positivist case research method and, hence, we strongly encourage IS researchers to follow their respective approaches.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 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 it