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Record W1996488581 · doi:10.5555/2820518.2820567

Do onboarding programs work

2015· article· en· W1996488581 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMining Software Repositories · 2015
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOnboardingStaffingComputer scienceWork (physics)Open sourceSoftware engineeringSoftware bugSoftwareKnowledge managementData scienceEngineeringManagement

Abstract

fetched live from OpenAlex

Open source software systems rely on community source code contributions to fix bugs and develop new features. Unfortunately, it is often difficult to become an effective contributor on open-source projects due to the complexity of the tools required to develop and test new patches and the challenge of breaking into an already-formed social organization. To help new contributors learn their development practices, OSS projects have created on boarding programs that, for example, identify easy 'first bugs' and mentor new developers' contributions. However, we found that developers who join an organization through these programs are half as likely to transition into long-term community members than developers who do not use these programs. Measuring the impact of these programs is important, as coordinating and staffing on boarding projects is expensive. This paper examines on boarding programs employed by Mozilla and demonstrates that they are not as effective at transitioning new developers into long-term contributors as might be hoped, although developers who do succeed through these programs find them valuable.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.274
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it