Bibliographic record
Abstract
This article presents a personal perspective on an academic and research vocation spanning a period of over 45 years. It starts with my early involvement in geography and climatology and terminates with my recent experience in a large interdisciplinary research venture. The presentation highlights, with specific examples, the importance of mentors. Also emphasized is the indispensable input of colleagues and graduate students to successful research endeavours. Most of my career has been centred on McMaster University, and I naturally draw on my experiences there. There have been great changes in the research world over the past few decades. Although the number of faculty and graduate students at McMaster remained relatively constant, the research output per person more than doubled. This is attributed in large part to the accelerating technological advancements in our ability to measure and our ability to process and manipulate data. In the environmental sciences, this has revolutionized the spatial and temporal scope of the scientific questions that can be addressed. Such major changes have stimulated a marked trend towards interdisciplinary research that has evolved from mainly wishful talking to active pursuit in a search to understand complex environmental interactions. Important among these is gaining insights into the processes and feedbacks driving climate change, whether natural or anthropologically induced. Equally important is gaining an understanding of the potential impacts resulting from climate change. My perception of my successes, failures and near misses divides chronologically into three periods that cover research in the early years, research in the central subarctic and research in the Mackenzie River Basin.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".