From Epistemic Bubbles to Generative Possibilities: Knowledge Leadership and Knowledge Mobilization for Child and Youth Care Practicum Education
Bibliographic record
Abstract
Child and Youth Care (CYC) Practicum Education (CYCPE) operates in more than 40 public postsecondary institutions (PSI) across Canada. CYC educators instruct and assess, while supervisors mentor thousands of students at child, youth, and family-serving organizations. As an emerging profession, CYC does not yet experience well-established governance, widespread postsecondary research infrastructure, nor public recognition, leaving CYCPE with threats to its credibility and existence. Despite individual CYC educators’ and programs’ extensive professional knowledge, we lack CYC-specific CYCPE organizational knowledge. This problem of practice (PoP) limits CYC educators’ ability to inform, improve, and innovate upon CYCPE’s design and delivery. This organizational improvement plan (OIP) positions CYCPE as an organization, to propose change initiatives that will disrupt its epistemic bubble. A critical postmodern (CPM) perspective forefronts tensions generatively. Organizational culture and discourse theory’s concepts provide a framework to analyze the PoP. Knowledge Leadership (KL)—within a Distributed Leadership (DL) higher education context, along a River Change Model (RCM) change process—propels a change initiative toward a desired state. Organizational knowledge creation and knowledge mobilization (KMb) expands CYCPE’s possibilities. By way of a CYCPE Consciousness-Raising Campaign, two streams of faculty-led change activities are detailed. This inquiry provides a novel perspective on CYCPE’s organization; syntheses of CYCPE’s extant data; application of KL in experiential education; and modification of Outcomes Harvesting (OH) to measure the change initiative’s contributions to a complex context. Ultimately, this inquiry is a call to action, for CYC educators to create and mobilize organizational knowledge, to benefit CYCPE’s complex design and delivery.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| 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".