Online Hunting, Gathering and Sharing – A Return to Experiential Learning in a Digital Age
Bibliographic record
Abstract
Learning through a collective experience by taking part in group activities, such as hunting, gathering, and sharing, has always been a natural, “organic,” and “experiential” process where new skills and knowledge, if benefitting the whole group, are accepted, shared, and propagated. Nevertheless, in industrialized societies where specific knowledge and skills are an economical and societal necessity, the learning economy has largely moved to a model where the teachers “harvest” selected knowledge and “put it in a basket” from which students are expected to take from and learn. This learning model has permeated the 21st century digital world, where the main promoted advantage of these new learning environments is still the “individualization of learning,” which can result in a very solitary and isolated endeavor; however, it doesn’t have to be the case. An example of a successful online university course suggests that carefully crafted online instructional design strategies can contribute to a flexible and rich experiential learning environment. Although they might be physically disconnected, it is possible for learners and a teacher to remain closely interconnected, engaged, and accountable for both individual and group success in knowledge "hunting, gathering, and sharing" activities in a digital age.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".