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Record W3025952062 · doi:10.33137/jaste.v5i1.34277

Coltan Mining and Internet Privacy

2020· article· en· W3025952062 on OpenAlexvenueno aff
Fahad Atif, Shawn Khan, Adriel Gobin

Bibliographic record

VenueJournal for Activist Science and Technology Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetInternet privacyAction (physics)Information privacyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This research-informed action project focuses on coltan mining and online privacy. In particular, the atrocities being carried out in foreign countries in pursuit of coltan and the risks of privacy invasion one faces when browsing the internet. Although at first it may appear that coltan mining and internet privacy are entirely separate issues, we recognize that these issues are part of larger network, and thus, they are related to each other via many intermediate actants. Through surveys taken at Erindale Secondary School, we learned that most people are significantly uninformed in regards to both topics. In response to these appalling results, the research team created three YouTubeTM videos about coltan mining and internet privacy which included numerous facts about the issues and the actions that one can take. Additionally, a FacebookTM page was created to draw attention to the issues. The FacebookTM page has over 100 likes and the YouTubeTM channel has over 1400 views.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.013
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.000

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 head0.076
GPT teacher head0.451
Teacher spread0.375 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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