MAPLE SYRUP AND CLIMATE CHANGE IN ONTARIO: ASSESSING TRANSDISCIPLINARY RESEARCH ACROSS MULTIPLE, RELATED PROJECTS
Bibliographic record
Abstract
The aim of this research was to critically evaluate the transdisciplinary process being utilized on the SSHRC and related research projects being led by Dr. Brenda Murphy. The approach was two-fold: first, a document analysis was performed using secondary data, and second, a questionnaire was conducted based on six themes that emerged from the literature. These themes were: Degree of Collaboration, The Value of Working Together over Time, Mutual Learning, Integration of Team Members, Complexity of the Problem Being Investigated and Bridging the Research-Societal Gap. Based on findings in the literature and responses to the questionnaire the themes were assessed as strengths or challenges. The strengths that emerged were: Degree of Collaboration, Complexity of the Problem Being Investigated and Bridging the Research-Societal Gap. The remaining themes (The Value of Working Together over Time, Mutual Learning and Integration of Team Members) had traits indicative of challenges to the research. Understanding the successes, challenges and solutions to challenges in transdisciplinary research is crucial to advancing this methodology in the academic realm. By including a wide variety of knowledges and perspectives transdisciplinary research is ideal for tackling the increasing number of complex problems, including climate change.
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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".