joc-open-science-and-transparency — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited joc-open-science-and-transparency (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
JoC requires a Data Availability Statement on every article and offers Open Science Badges for open data, open materials, and preregistration. Transparency is not an afterthought — build the statement and supporting materials as you write so submission and any badge claim go smoothly.
under what conditions they can be accessed, and — if they cannot be shared — why, with instructions for how others might obtain them.
regenerate every reported result; master script + README + pinned versions + seeds; for automated text measures, include the human-validation materials.
evidence tables, de-identified excerpts) with access controls where needed (QDR for controlled access).
service, or legal restrictions by the provider).
Because JoC spans communication research broadly, "transparency" differs across traditions. Match the deposit to the method rather than forcing one template:
| Method | What a JoC referee wants deposited | Badge most relevant |
|---|---|---|
| Survey / message experiment | data + codebook + stimuli + analysis script | open data + materials + preregistration |
| Content analysis | codebook + coder instructions + reliability subsample + texts | open materials (+ open data) |
| Computational / text-as-data | corpus or query, model/version, seeds, human-validation set | open materials + open data |
| Qualitative / critical | coding scheme + evidence tables + de-identified excerpts | open materials where ethics allow |
For computational measures, the human-validation set is itself the evidence that the automated label means what the paper claims — depositing the classifier without it leaves the construct unverified. Confirm badge mechanics against the journal's current guidance.
A computational content analysis of 40,000 news articles (illustrative) on climate coverage hits a familiar wall: the texts are copyrighted and the feed bars redistribution. The transparency path: (1) deposit the codebook, article IDs/URLs, query parameters, and analysis code so a same-license reader reproduces the pipeline; (2) deposit the human-validation sample, shareable derived data; (3) write a DAS naming the restriction, provider, and access route, and offering de-identified derived features (frame proportions per article) so modeling re-runs without raw text — earning open-materials credit and an honest, hedged DAS.
【Data Availability Statement】drafted? repository + identifier or exemption? [Y/N]
【Reproduces tables/figures?】master script verified locally? [Y/N]
【Badges sought】open data / open materials / preregistration (materials staged?)
【Documentation】README + provenance + seeds + pinned versions? [Y/N]
【Restricted data?】exemption note + access path + synthetic data?
【Qualitative transparency】coding scheme / evidence documented? [Y/N/NA]
【Next】joc-review-process../../resources/external_tools.md — reproducibility tooling and qualitative-transparency options (OSF, QDR)../../resources/official-source-map.md — Data Availability Statement requirement + Open Science Badges~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.