Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the rationale and use of subjective personal introspection (SPI) as a methodological approach. Design/methodology/approach SPI was utilised to develop a “narrative” of the author's own “action‐oriented” research experience within a multisector collaborative venture established by 13 partner organisations representing the academic, pharmaceutical industry and government sectors. The “confessional” stance that the study assumes describes some of the perceived tensions enacted during field work. The SPI approach is theoretical and reflective, as well as descriptive and analytical, in reporting the antecedents, actions, and outcomes in action‐oriented research. Findings Because the focus of the paper is subjective, personal, and introspective, it does not illustrate “findings” about multisector collaboration, but rather reflections and insights about the way the research was conducted. Practical implications The paper widens the forum for incorporating SPI beyond the consumer behaviour context to the context in which action‐oriented researchers incorporate introspection in their study of organisations. Originality/value The paper goes some way to bridging the gap between SPI and reflexivity (if there is indeed a gap) and it causes qualitative, action‐oriented organisational researchers to contemplate a number of questions: what is the role of the researcher; what is the source of their authority to narrate and what are they authorised to recount; and what are the consequences of this?
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 | 0.219 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.080 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".