Triangulation in Canadian doctoral dissertations on aging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Triangulation has been increasingly used in gerontological research. It is unknown, however, whether this approach has been implemented by emergent scholars in the field. The goal of this article is to provide review of Canadian doctoral dissertations in the field of aging with the following questions in mind: Is triangulation common in doctoral dissertations on aging in Canada? What triangulation strategies are used by doctoral students? What implications to doctoral education could these data have? The authors searched the ProQuest Dissertations & Theses database (from May 1966 to November 2011) and reviewed 66 doctoral dissertations that met the inclusion criteria. The findings revealed recent proliferation in use of triangulation strategies in doctoral dissertation research on aging in Canada. Methodological triangulation, data source triangulation and multiple triangulation were found as the most widely used triangulation strategies by emergent scholars, whereas theoretical and investigator triangulations were less common. Implications for gerontological research and education are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it