Bibliographic record
Abstract
Open has defined my professional career in every way imaginable: for almost ten years now it has been the motivating force in my career, the mode in which I work, and the subject that I research.As a result, today I live and breathe open-but it has not always been this way.What follows is the story of how open proved itself to me.It is a story that demonstrates how participating in open projects and processes can lead to unexpected opportunities.In my case, these opportunities have taken me on a journey from an itinerant software developer, to a workshop instructor in over a dozen countries, to a PhD at Stanford University, and finally to an academic career.These opportunities have lead to my contributing thousands of lines of code, over a dozen research articles, two edited books, countless workshops for journal editors, and the teaching of undergraduate and graduate students.More importantly, they have allowed me to contribute back to the region of the world from which I emigrated as a child by helping to amplify the voices of Latin American scholars worldwide.This unexpected journey started in January 2006 when I took a job at the open access Journal of Medical Internet Research (JMIR), where I was responsible, among other things, for upgrading the journal to the latest version of the Public Knowledge Project's (PKP) open source software Open Journal Systems (OJS).JMIR has been a pioneer in open access, and it was doing so using open source software as its foundation.Unbeknownst to me at the time, this convergence of open source with open access would become the defining trait of my career.
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 | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.050 | 0.030 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.039 | 0.021 |
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".