An Approach to Support Human-in-the-Loop Big Data Software Development Projects
Bibliographic record
Abstract
There is a lack of approaches and tools to support the development of projects in which humans and machines (e.g., machine learning algorithms) need to collaborate to achieve a specified goal. Specifically, given a set of software development tasks to develop a project collaboratively, how can these tasks be assigned to humans or machines to perform each task most efficiently and effectively? Such understanding is essential to support new methodologies for developing human-in-the-loop approaches in which machine learning automated procedures assist software developers in achieving their tasks. This paper describes our work in progress towards providing an approach to guide the assignment of tasks in developing human-in-the-loop big data (science) software development projects. The paper provides several contributions, including the provision of (i) a human-in-the-loop approach for the development of big data software development projects; (ii) the application of the approach to two case studies; (iii) a discussion of implications and research 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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".