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Record W4323925049 · doi:10.1007/s10664-022-10277-5

Registered reports in software engineering

2023· editorial· en· W4323925049 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

VenueEmpirical Software Engineering · 2023
Typeeditorial
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSoftware engineeringComputer scienceEngineeringSystems engineering

Abstract

fetched live from OpenAlex

Registered reports are scientific publications which begin the publication process by first having the detailed research protocol, including key research questions, reviewed and approved by peers. Subsequent analysis and results are published with minimal additional review, even if there was no clear support for the underlying hypothesis, as long as the approved protocol is followed. Registered reports can prevent several questionable research practices and give early feedback on research designs. In software engineering research, registered reports were first introduced in the International Conference on Mining Software Repositories (MSR) in 2020. They are now established in three conferences and two pre-eminent journals, including this one (EMSE). We explain the motivation for registered reports, outline the way they have been implemented in software engineering, and outline some ongoing challenges for addressing high quality software engineering research.

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)0.002
metaresearch head score (Gemma)0.147
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.147
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.005
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 head0.028
GPT teacher head0.300
Teacher spread0.272 · 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