Can an Academic Journal Promote Radical Scholarship?
Bibliographic record
Abstract
In our first editorial, published in the February issue of 2017, we explained our hope for the journal to become a space in which innovative thinking and boundary-pushing scholarship could be voiced and shared.As we, the new editorial team, learned the day-to-day tasks of running a journal in the months that have followed, we have also been pondering how to actualize those aspirations.The dilemma, to put it bluntly, is this: We aspire to be a journal that publishes radical scholarship that pushes the edges of received knowledge in social work, but the social work academe that generates the authors-and of which we and the journal are a part-does not promote or support such scholarship.Notwithstanding the Hollywood trope of the ardent scholar driven by the pursuit of pure knowledge unimpeded by distractions and detractors, the reality of the academe is that we write and publish not only to follow our scholarly vision and ideals, but because it is a necessity for the particular type of public recognition required for job security and career advancement.Putting aside the issue of the enormous privilege inherent in tenure-track faculty jobs, sustained by the labor of an army of poorly compensated contingent faculty, it must be acknowledged that the system of in-orout tenure is structured so that job security requires career advancement, and career advancement is measured most often by a narrow definition of productivity.Social work academics know that productivity means that numbers-of published manuscripts, of journal rankings by impact factor, and of funded grants and dollars granted-are what matter most, most often.We are also aware that impact factor, H-index, and other such quantitative metrics used to account for productivity have been repeatedly critiqued as biased measures that clearly valorize only particular types of scholarship.Social work scholarship, in other words, is bound within and delimited by the business models that govern the institutions in which we work.The business model of the increasingly corporatized universities is one in which funding directs scholarship, and metrics designed for and about such fundable scholarship shape the viability of its faculty and the form, content, and tenor of their scholarship.Ideas such as slow scholarship argue against such academic business models (http://web.uvic.
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 | 0.057 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.062 | 0.035 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.024 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 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".