Bibliographic record
Abstract
Although many assume that conducting cutting-edge research in a particular field is synonymous with leadership, I challenge this assumption. I believe it is fair to say that just as many managers are not leaders, neither are all researchers. For many people, trying to learn the research process and to complete that first (or seventh) study seems to be an overwhelming task. In the world of research, completing the study is just the first step ... making the research come alive and using it to build capacity for future science and scientists and to tell stories that capture policymakers’ attention and ultimately lead to policy change, are what it is all about. Over the years, I have come to understand that leadership in research is about building relationships. Initially my relationships were with thesis supervisors and other deities whose words and mentoring were sacred. Later, as the playing field began to balance and I had some research successes of my own, my collaborative relationships expanded to include colleagues engaged in research and future researchers. In the past several years, these relationships have developed further to include individuals who are challenged to use the scarce information from research to make decisions or to guide discussion of alternative policy choices. Each step of the way has brought new learning and clearer insight into what my role in the relationship should and should not be. Understanding myself and my individual quirks has helped me learn how to manage myself (most of the time) so that my behaviors do not encumber building these relationships. Without realizing it, I have been influenced by the leadership principles hypothesized by Kouzes and Posner (1997). The five leadership principles include: modeling the way, inspiring shared vision, challenging the process, enabling Leadership in Research: About Building Relationships
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Incentives · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.134 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.018 | 0.164 |
| Scholarly communication | 0.043 | 0.055 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".