Bibliographic record
Abstract
This chapter draws on a PhD study of identity construction processes of recently appointed managers in a Canadian organization to explore the relational hyphen spaces between the researcher (one of the authors, Nancy) and the participants to the study. The data was collected using shadowing, a technique that involves closely following a member of an organization in her or his daily activities, which is useful to access emerging practice in real time and space. Recent studies frame shadowing data as an intersubjective construct in which both researcher and participant actively participate, and in which interaction is central. Fine argues for an examination of research processes that attends to the lived experiences of participants. Pullen highlights the fact that researchers do not merely perform research but also (re)produce themselves in doing so, and criticizes the absence of such discussion in most research accounts. To address this, we propose to consider shadowing as liminal space(s), and we explore what happens in transition, in-between, and in/at the margins of the institutional spaces of shadowee and researcher (Nancy). The paper’s contribution is twofold: It provides input on shadowing’s possibilities for exploring the interactional dynamics of hyphens spaces, and it addresses some of the ethical issues that the method entails.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".