Promisingness Judgments as Facilitators of Knowledge Building
Bibliographic record
Abstract
Abstract: Knowledge creation depends on pursuit of promising possibilities. This paper reports a case study of a graduate-level course, with promisingness judgments incorporated as an explicit goal of course work. The top-level goal for the course was to have students take collective responsibility for “the creation of an assessment of collaborative knowledge creation. ” This paper presents the pedagogical design of the course, describes technological affordances to support promisingness judgments, and discusses preliminary findings. Promisingness Judgments in Knowledge Building Whether it is industrial designers working on a new product, scientists planning the next experiment in a research program, or policy-makers planning social legislation, decisions must be made about the investment of resources and effort in further development of ideas. “Ideas are the easy part, ” says a high-profile design group (Fahrenheit 212, 2010), noting that ideas are usually in abundant supply. Going from an initial idea to an innovation, however, requires time and effort, and calls for evaluation about the potential fruitfulness of ideas in an uncertain future. Bereiter and Scardamalia (1993) refer to such evaluations as “promisingness judgments” and have argued that they play an essential role in creative expertise. In explaining creative processes, Gardner (1994) also describes the process of identifying promising ideas as bringing out “discrepant elements ” and
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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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".