Rejoinder
Bibliographic record
Abstract
It has become essential and urgent that significant actors in the management field of research become aware of the current rejection of previously accepted philosophical caricatures. The unrealistic though “tidy” paradigmatic dichotomy, positivism/quantitative/deduction versus interpretivism/qualitative/induction, is being rejected. Instead, a growing and “untidy” consensus is emerging that helps to position grounded theory (GT) in the research landscape. This growing consensus includes perspectives that range from nomothetic to idiographic and highlights data-driven exploratory approaches in opposition to theory-driven confirmatory approaches. While the foundational pillars of GT (emergence, theoretical sampling, and constant comparison) have to be respected when conducting a GT study, there certainly is plenty of room for creativity in the implementation of a data-driven exploratory GT approach. GT is not limited to an all-encompassing method for qualitative or interpretive research: It is much broader and may be applied from various philosophical perspectives that range from nomothetic to idiographic.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".