Balancing Openness and Interpretation in Active Listening
Bibliographic record
Abstract
Active listening is an important communication skill in a variety of disciplines and professions, including the profession of Educational Development. In our roles as educational developers, we engage in a variety of processes, all of which rely heavily on the practice of active listening. Emerging strategies of active listening praxis have allowed us to highlight the risks involved in applying techniques of active listening while engaging in our day to day practices as educational developers. In this paper, we share a short historical review of theories and approaches to active listening and identify its two inherent orientations: humanism and professionalism. We highlight openness and interpretation as two pillars of active listening and explore ways of maintaining a balance between those pillars as they relate to techniques of humanism and professionalism. We explain aspects of the interactive session we facilitated during STLHE 2015 where, as a group, we named and discussed ways that active listening could go wrong. We explore ways that we, as educators and educational developers, might recognize when we may be foregrounding technique over listening, and how to correct some of these issues.
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 | 0.049 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.081 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".