Triangulation in Canadian doctoral dissertations on aging
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.
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 | 0.181 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.060 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".