MétaCan
Menu
Back to cohort
Record W6926644050 · doi:10.25547/t8dq-k791

Review of Netlytic

2021· article· en· W6926644050 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueElectronic Textual Cultures Lab · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiphtheria, Corynebacterium, and Tetanus
Canadian institutionsnot available
Fundersnot available
KeywordsRSSSet (abstract data type)Cloud computingData setSocial network (sociolinguistics)Social media

Abstract

fetched live from OpenAlex

<p>Developed by the Social Media Lab (socialmedialab.ca) at Ryerson University, Netlytic (netlytic.org) is a cloud-based text and social network analyzer that aims to help uncover social networks from online conversations. Netlytic has the ability to import data from a number of sources—including Twitter, Facebook, YouTube, Instagram, text files, RSS feeds, and cloud storage—and advocates for ease of use as it requires no programming skills. However, Netlytic does require some underlying knowledge about the structure of the data you are working with. All in all, I found Netlytic to excel at carrying out exploratory analysis. In my opinion, more focused and descriptive analysis would require a different set of more specialized tools. Netlytic integrates a data harvester with a streamlined user interface. The result: ease of use and convenience.</p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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