Bibliographic record
Abstract
Practitioners and theorists have given attention recently to the role and status of research activities in Canadian university continuing education units. For individuals in units that are increasing the proportion of their organizational activities devoted to research, there will be an ongoing process of cognitive change and development as a new organizational culture emerges. Sensemaking is used in this article as a heuristic for exploring the process of incorporating and developing research activities in university continuing education units. Sensemaking is the cognitive process of justifying or legitimating a decision or outcome after the decision or outcome is already known. It is associated with organizational models that reject an exclusively rational decision-making paradigm of organizational action. Sensemaking recognizes the centrality of the following elements in the interpretation of research activities and their relationship to organizational life: time, identity construction, and the ongoing creation of context. The authors provide an extended reflection on the process of meaning-making that may be experienced by individuals as conventional research becomes a more important part of organizational life. Such a reflection may support and inform the change process as it occurs in university continuing education units.
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 | 0.073 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.014 | 0.135 |
| Scholarly communication | 0.036 | 0.023 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".