The Importance of Metacommunication in Supervision Processes in Higher Education
Bibliographic record
Abstract
In daily language use, we sometimes comment on the conversation with phrases such as “What do you mean by saying that?” or “That was nice of you to say.” This communication about the communication is sometimes labeled as metacommunication. It can be used for many different purposes; for instance, to try and clarify or appraise something that has been said in a conversation. In higher education, a recent empirical study finds that discussions between the student and supervisor about the supervision process have a positive impact on the quality of the communication. Despite this, we know little about the specific metacommunicative mechanisms that may be of importance in supervision. One reason is that most definitions of the metacommunication concept are vague and inconsistent. The goal of this paper is therefore to review a broad range of research literature about metacommunication in an attempt to develop a more comprehensive and complex definition. These perspectives are then used to discuss what specific types of metacommunication might facilitate good supervision in higher education. It is suggested that one should distinguish between metacommunication as part of a transparent communication style and metacommunication about the collaboration period in supervision.
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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 | 0.020 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".