A Shoestring Grassroots Approach to Publishing an Open Educational Resource Engineering Textbook
Bibliographic record
Abstract
The lack of affordable and accessible education is one of the major obstacles inhibiting the upward social mobility of New Yorkers from low to middle socioeconomic classes.Students from lower income, underrepresented and first-generation college households in an urban setting, are particularly affected by the rise of higher education costs, which further marginalizes the members of these communities.The availability and adoption of good quality Open Educational Resource (OER) textbooks, has the ability to significantly ease the economic burden on the student population; however, upper level engineering text books, available as OERs, are scarce and vary in quality.This case study presents a grassroots approach for the creation of a Soil Mechanics Engineering Textbook on a shoe string budget.This ongoing work is being done by putting into play an array of interdisciplinary resources, available within the New York City College of Technology and the City University of New York.These include library collaborations, communication design professionals, students, undergraduate research programs, surplus technician funds and professional community goodwill.The result is a well-rounded, visually engaging and appealing, peer reviewed OER textbook, which when published will become available free to any student and faculty member worldwide.This translates to direct savings of approximately $112 per student, at local and/or national higher education institutions that adopt the textbook for their entry level soil mechanics course.The author estimates that when adopted at the New York City College of Technology, the entire cost of publication of the textbook will be recovered through student savings within three semesters, by conservative estimates.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".