Introducing <i>Minor Revisions</i> , a podcast from <i>Politics & Space</i>
Bibliographic record
Abstract
In our editorial announcing the re-imagining of this journal as EPC: Politics & Space, my fellow editors and I committed to supporting "scholars in a range of disciplines, geographical locations, positions and ranks within and beyond the academy" (Daley et al., 2017: 5).Our new initiative, a podcast entitled Minor Revisions, serves that goal (https://journals.sagepub.com/page/epc/ collections/podcasts).Minor Revisions is intended to demystify the process of writing for academic journals.Each episode will feature authors who tell the 'behind the scenes' story of how they developed their article and succeeded in the review process.They will reveal how their article came about, including their research and writing strategies.Authors will reveal what decisions they made about literatures to draw upon, how they went about choosing a journal for submission, how they collaborated with editors and reviewers to refine their submission, how they negotiated expectations regarding writing style, article structure, and forms of English, what challenges they overcame as they wrote, and their hopes for their article, now that it is out in the world.We see Minor Revisions as supporting at least three overlapping groups within our community: Early career researchers as they learn to write for journal publication, by providing an easily-accessible source of knowledge and insights that are often tacit and difficult for some to access; Scholars who are underrepresented in international, English-language journals and are trained in other publishing traditions and cultures, by offering a forensic understanding of the publishing process in this particular journal and similar ones; Readers of the journal and other critical social science journals, by providing them with more insight into how the articles they read are written and published.At Politics & Space, we are committed to being as open and as accessible as we are able to be.Therefore, each episode of the podcast will be accompanied by a written transcript, which can be downloaded from the webpage above.Each of the articles discussed in the podcast will also be made available free to access and will be accessible via the podcast webpage.And, of course, Minor Revisions is widely available wherever you find your podcasts, including Apple Podcasts, Spotify, Soundcloud, Stitcher, and Google Podcasts.
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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.117 | 0.088 |
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".