Researching Lived Experience, Second Edition
Bibliographic record
Abstract
Bestselling author Max van Manen’s Researching Lived Experience introduces a human science approach to research methodology in education and related fields. The book takes as its starting point the "everyday lived experience" of human beings in educational situations. Rather than rely on abstract generalizations and theories in the traditional sense, the author offers an alternative that taps the unique nature of each human situation. First published in 1990, this book is a classic of social science methodology and phenomenological research, selling tens of thousands of copies over the past quarter century. Left Coast is making available the second edition of this work, never before released outside Canada. Researching Lived Experience offers detailed methodological explications and practical examples of inquiry. It shows how to orient oneself to human experience in education and how to construct a textual question which evokes a fundamental sense of wonder, and it provides a broad and systematic set of approaches for gaining experiential material which forms the basis for textual reflections. The author: -Discusses the part played by language in educational research-Pays special attention to the methodological function of anecdotal narrative in research-Offers approaches to structuring the research text in relation to the particular kinds of questions being studied
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".