Getting Your Work Published: Advice for New and Developing Scholars
Bibliographic record
Abstract
Academic writing and publishing are skills that are vital to the success of new scholars; however, existing writing supports are limited in terms of supported languages and genres (Strobl et al., 2019). To optimize these kinds of support, institutional units need to exist in tandem with non-institutional supports, such as peer-to-peer collaboration (Gopee & Deane 2013). Writing skills associated with research articles also need to be discussed in greater detail and receive a greater share of support (Strobl et al., 2019). Such support would benefit all graduate students (Ma, 2019). The authors of this manuscript have come together to offer advice and considerations to early-career scholars looking to publish for the first time. These include building confidence, publishing with peers, understanding diverse journals, developing one’s writer’s voice, and pursuing diverse publishing opportunities.
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 | 0.042 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.042 | 0.058 |
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".