Examining academic-consulting in Canada : conditions for successful academic-consulting engagements
Bibliographic record
Abstract
Academics are becoming increasingly involved in academic-consulting, whereby they engage in consulting projects outside of their regular academic duties.However, little research has been done to determine the implications and barriers to success for such engagements.This unique research provides new knowledge on the area of academicconsulting by providing a practical review of the success conditions for academicconsulting.Quantitative and qualitative research methods were employed to gather experiential data from 20 Canadian survey and interview participants, all of who had experience with academic-consulting.Based on the results of the exploratory research, there are four key implications that must be considered before engaging in academic-consulting: motivations, university culture and policies, academic experience in consulting and project specific conditions.In conclusion, it was determined that all of these conditions must be considered by not only the academic involved, but by all stakeholders of the engagement.Furthermore, it is concluded that although academics have certain predispositions and environmental factors that affect their success, like any other consultant, the success of an academicconsultant depends on their ability to effectively consult.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.004 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".