Products of theorizing—towards native theories of emerging information technologies
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
Everything about theory is contested in our field today.First, there's the role of theory in IS research.Is it necessary for research publications to make a theoretical contribution?What is a theoretical contribution?Should we devote more effort to building theory or to testing theory?Can you test theory with qualitative data?Is theory even relevant if one is doing research with analytic or machine learning methods?Second, there's the issue of how to build theory.Can you build theory with quantitative data?Do you need empirical data to build theory?How important is the literature review (and what type of review) in theory building papers (Leidner 2018)?What is the role of "disciplined imagination" (Weick 1989) in IS theorizing?Is "armchair theorizing" permissible, and, if so, when?Third, there's the issue of what theory looks like.Is a good theory a boxes-and-arrows diagram followed by themore-X-the-more-Y type propositions?Or are there other persuasive ways to present theories?One possibility might be events, conditions and mechanisms.Another might be narratives of how things happen.And, perhaps most important of all, how do we as a field cope with multiple, overlapping, possibly inconsistent, theories of the same phenomenon?
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| 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