Open Textbook Proof-of-Concept via Connexions
Bibliographic record
Abstract
To address the high cost of textbooks, Rice University’s Connexions and the Community College Open Textbook Project (CCOTP) collaborated to develop a proof-of-concept free and open textbook. The proof-of-concept served to document a workflow process that would support adoption of open textbooks. Open textbooks provide faculty and students with a low cost alternative to traditional publishers’ textbooks and can help to make higher education more affordable. Connexions provides a publishing platform for open textbook projects. The CCOTP acted as a liaison between community college faculty, open textbook authors, and Connexions. Challenges to the production and adoption of open textbooks include 1) faculty members’ and students’ expectations of high production quality and ancillaries for open textbooks, 2) methods for documenting and maintaining control over various versions, and 3) the process of converting existing open content to digital and accessible formats. Connexions holds promise as a means to overcome these challenges. Connexions identified lessons learned about open textbook production, such as the importance of a style guide, the advantage of assembly-line workflow, and the importance of naming conventions and standard math authoring tools, Connexions also identified lessons learned about open textbook use by students and faculty, e.g., the value of availability and customizability, the importance of interactivity, the difference in how faculty and students view modularity, and the importance of textbook reading navigational aids. The authors note that the CCOTP recommends using Connexions as the common repository for open textbook content in an effort to provide greater national and international access.
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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.107 | 0.026 |
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".