Bibliographic record
Abstract
This manuscript describes a technique which facilitates the documentation of research participant stories interpreting their experiences in response to a research question. From a Narrative Inquiry approach interview protocols were developed based upon the exploration of a research question. The technique may be applied when gathering qualitative data in one-on-one interviews. Each interview protocol provided consistency across a number of interviews; but also allowed for flexibility of responses by the research participant within their respective interviews. This document provides a description of a technique which addresses the conundrum of consistency and flexibility. Four different research projects are described in this manuscript. The specific interview protocol is presented and it is shown how the protocol serves to address the project’s research question. This document concludes with a description of how these techniques may be employed, in general, to contribute to the exploratory investigation of a research topic in business and management studies.
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 | 0.170 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 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".