Nurturing a Culture of Responsible Conduct of Research to Support Safe Disclosure
Bibliographic record
Abstract
Pressures to publish, which are prevalent in higher education, can lead to research misconduct and, in the absence of clear, safe disclosure policies—and mechanisms and structures to support them—individuals affected by research misconduct may fear retaliation when speaking up. This Organizational Improvement Plan examines how to foster a climate where individuals feel supported and are encouraged to speak up if they witness research misconduct at Forest University (a pseudonym), a research-intensive university in Ontario, Canada. In alignment with my values, the change process is guided by authentic and ethical leadership perspectives. The Change-Path Model, supported by Beckhard and Harris’ Change-Management Process, is the change framework to address the Problem of Practice (PoP). Krüger’s Iceberg Model of Change and an adapted readiness rubric have been used to deepen my understanding of the organizational culture and to identify expected and unexpected resistance points. The Plan-Do-Check-Act cycle will be used to determine where refinement is needed. Forest University has a large, diverse population of students, faculty, and staff. A working group will be assembled using shared equity leadership to ensure a range of lived and learned experience to address the PoP and support the change. The proposed solution takes a hybrid approach that focuses on introducing mechanisms and structures to support policy, including hiring a dedicated role to develop training and education, serve as an intake for research misconduct concerns, and to keep policy up to date.
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 | 0.094 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".