Teaching the "Information Creation as a Process" Framework through Video Gaming
Bibliographic record
Abstract
The Inheritance is a video game that uses storytelling to teach the ACRL framework Information Creation as a process. The game can be an engaging way for librarians to teach the abstract concept. The objective is not only about reaching the end of the game safely, but also, about gathering the necessary items along the way to solve the mystery. You will have a checklist of items that you must gather in order to make it through the game. We used Twine, an application made for game creation, to visualize our concept. This game gets quite complex as you go, so it is recommended that you keep a piece of paper on hand to track your progress. Also, if you do not make it through the game on your first play-through, it is recommended that you try again! Finally, if you choose to listen to the narration clips throughout the game, it enhances the immersive experience. Overall, have fun playing our game! The goal was to allow the user to enjoy the process, rather than putting so much emphasis on reaching the end. Now you can simply download the zip file, open it, and click on index.html.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".