Bibliographic record
Abstract
In any research study, researchers situate themselves, either explicitly or implicitly, within a variety of frameworks when studying phenomena. From a research perspective, the study will be more robust if these frameworks and the accompanying assumptions are compatible with each other; otherwise, the project may lack coherence. Ricoeur offers a methodological perspective-that is, an interpretive theory as reflected in mimesis, which is congruent with his ontological theory of self identity (ipse- and idem-identity). To illustrate Ricoeur's frameworks when researching the self identities, I use examples from a research study in which I asked senior nursing students to explore their experience of becoming a nurse. I do not intend for this article to be a comprehensive research report, but I present it as an exemplar of how Ricoeur's ideas can guide other researchers studying self identity. I labelled my study a narrative research project and assumed that becoming a nurse means developing a self identity as a nurse. While self identity is often framed in psychological terms, Ricoeur uses a philosophical perspective when exploring this concept. I conclude the paper by suggesting (a) that Ricoeur can guide any project in which researchers ask participants to describe "becoming" a person with illness, sickness or disease, and (b) that educators of healthcare professional students can improve the educative experience by purposefully considering how a student's ontological self affects that student's practice.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".