Dana, N. F. (2013). Digging deeper into action research: A teacher inquirer’s field guide. Thousand Oaks, CA: Corwin.
Bibliographic record
Abstract
This book is one in a series conducted by Nancy Dana, professor in the School of Teaching and Learning at the University of Florida. Specifically, this book comes as a practical complement to her previous book ‘The reflective educator’s guide to classroom research: Learning to teach and teaching to learn through practitioner inquiry’ (Dana & Yendol-Hoppey, 2008) which is a comprehensive introduction. The title of the book briefly describes what the book is about. For example, the concept digging deeper and ‘field guide’ suggest a topic related to nature. Actually, according to Merriam Webster’s ‘field guide’ mainly refers to a manual to explore nature (Merriam-webster.com). The title also talks about ‘action research’ and ‘teacher inquirer’. Thus, the title of this book as a whole refers to a manual that will guide teacher researchers as they explore action research. This is an easy-to-read, instructive book which is mainly intended for school teachers. However, the advice for action research provided here can also be used for post-secondary level education. Since the main goal of this book is to provide guidance and useful tips for either novice teacher researchers or more experienced ones, this book does not contain a great deal of research field related terminology. Dana’s book is suitable for both novice and experienced researchers.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.026 |
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".