Replication Package of the Paper "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow"
Bibliographic record
Abstract
This replication package contains datasets and scripts related to the paper: "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow" Contributor_Classification.csv: contains the assignment of each contributor to a specific classification. The file contains the following columns: Date : contains the date of each comment Type: describe the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC) ID : specific ID of the comment Body : contains the body of the comment analyzed Url : link at each comments NumberPR : number of a PR Name_Contributor: contains the name of a contributor for each comment Contributor_classification: contains the assignment of a specific classification (academic, bot, ML expert, software engineer, unknown), obtained after manual analysis, for each contributor of a comment Contributors_ManualAnalysis.csv: contains the manual analysis performed by two authors to assign a classification for each contributor. The file contains the following columns: Contributors: contains the name of the contributor for each comment Link GitHub: contains the link to the GitHub page for each contributor # PR: contains the number of PRs in which a specific contributor is involved Annotator1: manual classification of the first annotator Annotator2: manual classification of the second annotator Final Classification: contains the final label (academic, bot, ML expert, software engineer, unknown)assigned for each contributor Organization: contains the organization, if any (Google, Hugging Face, Microsoft, OpenAI) ManualAnalysis.csv: contains the manual analysis performed regarding Comment Type, Nature of Comment and Artifact. The .csv contains the following columns: URL: contains the link to each comment NumberPR : number of pull request CommentType1: contains the classification of the first annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other) CommentType2: contains the classification of the second annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other) NatureComment1: contains the classification of the first annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment) NatureComment2: contains the classification of the second annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment) Artifact1: contains the classification of the first annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other) Artifact2: contains the classification of the second annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other) FINALCommentType: contains the final classification of the comment type after the resolution of the conflicts FINALNatureComment: contains the final classification of the nature of the comment after resolution of conflicts FINALArtifact: contains the final classification of the artifact after the resolution of conflicts Summary_PR.csv: contains the details about the composition of each PRs. The file contains the following columns: #PullRequest: contains the number of all pull requests analyzed #events: contains the number of all the events analyzed for each PR #comments: contains the number of all comments for each PR #Commit:contains the number of Commit for each PR PC: contains the number of PC for each PR RC: contains the number of RC for each PR Total_PR_Comments.csv: contains information about all comments analyzed. The columns are: Date: contains the date of each comment Type: describes the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC) ID: SHAn of the comment NumberPR: PR number Name_Contributor: contains the name of a contributor for each comment Body: contains the body of the comment analyzed Url: link at each comment The replication also contains a directory results in which there are quantitative results. The directory contains: Artifacts.csv: This file contains the results for the artifact. The columns are: #PullRequest: number of the pull request analyzed Article: contains the percentage of the occurrences of the article in the specific pull request Code: contains the percentage of the occurrences of the code in the specific pull request Commit: contains the percentage of the occurrences of the commit in the specific pull request Issue reference: contains the percentage of the occurrences of the issue reference in the specific pull request External link: contains the percentage of the occurrences of the external link in the specific pull request Review: contains the percentage of the occurrences of the review in the specific pull request Other: contains the percentage of the occurrences of the other in the specific pull request ßAlso, the Mean_Value row contains the mean value of all occurrences for each column (Article, Code, Commit, Issue reference, External link, Review, Other) CommentType.csv: this file contains the results for the comment type. The columns are: #PullRequest:number of the pull request analyzed Conventional review: contains the percentage of the occurrences of the conventional review in the specific pull request Initial implementation: contains the percentage of the occurrences of the initial implementation in the specific pull request Management: contains the percentage of the occurrences of the management in the specific pull request ML review: contains the percentage of the occurrences of the ML review in the specific pull request Other: contains the percentage of the occurrences of the Other in the specific pull request Also, the Mean_Value row contains the mean value of all occurrences for each column (Conventional review, Initial implementation, Management, ML review, Other) Contributor.csv: this file contains the results for the contributors. The columns are: #PullRequest: number of the pull requests analyzed academic: contains the percentage of the occurrences of the academic in the specific pull request bot: contains the percentage of the occurrences of the bot in the specific pull request ML expert: contains the percentage of the occurrences of the ML expert in the specific pull request software engineer: contains the percentage of the occurrences of the software engineer in the specific pull request unknown: contains the percentage of the occurrences of unknown in the specific pull request Also, the Mean_Value row contains the mean value of all occurrences for each column (academic, bot, ML expert, software engineer, unknown)
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 | 0.020 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.355 | 0.332 |
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".