Bibliographic record
Abstract
When reading through an article within this issue - the one by Cathryn Smith from Brandon University - I was delighted that she had revisited and expanded upon a pithy analogy I haven’t seen for a while. It was a phrase written by Kathryn Herr and Gary Anderson back in 2005 as they endeavoured to explain the challenges of engaging in Action Research during the dissertation process: That engaging in this methodology is like “designing the plane while flying it”. When I originally read Herr and Anderson’s simile, I had seen it as a rather humorous, yet negative one. Using wild exaggeration, the conclusion the authors seem to draw was that it was an impossible task – daredevil activity to say the least… or suicidal, more likely. Now, 13 years later, I am happy to see Dr. Smith take a pragmatic twist on this flight of hyperbole. Rather than seeing this aphorism as a mere “blow off” statement, scaring away potential researchers with its connotation of being a nonstarter, she looks at this phrase as a mere statement of the way things are. The trick is to understand the fact, and to ground your mindset around these parameters. Equally inventive, she parses out her work using aviation analogies, which allows her work to take wings.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".