Research Software Current State Assessment
Bibliographic record
Abstract
As the first position paper on research software (RS) in Canada, this report surveys and summarizes RS generally as an emerging field and as an area of professionalization, nationally and internationally, and documents strengths, challenges, and opportunities within the current RS ecosystem, as they pertain to the Digital Research Alliance of Canada (the Alliance). The purpose of this paper is to frame an understanding of the RS landscape and establish a general framework for conversation within Canada. This report complements The Current State of Research Data Management (RDM) in Canada and The Current State of Advanced Research Computing (ARC) in Canada reports. This report is intended to allow the Alliance to understand and build on the current state and facilitate a strategy that advances RS in coordination with other digital research infrastructure (DRI) elements to support research excellence in Canada. Findings and observations in this document, alongside the RDM and ARC Current State Assessment publications, are meant to provide background information to the Alliance analysts and management, the Alliance Board, and the Alliance Researcher Council, to support the development of the Alliance’s New Service Delivery Model (NSDM), Strategic Planning, and Funding Model Delivery.
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 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.033 | 0.013 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.020 |
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".