Bibliographic record
Abstract
wrote, "O, what a tangled web we weave…" (Marmion, 1808, 17).While Scott spoke of a tragic romance, in 2018, journalist Al Fonzi echoed these words in an incendiary New Times article accusing The Sierra Club, an "influential grassroots environmental organization", of being influenced by Russia.Fonzi went on to endorse fracking as a cleaner way of obtaining fossil fuels, appealing to those concerned about climate change by remarking how America had reduced its carbon emissions more than any other nation (Fonzi, 2018).He praised the industry's benefit to the economy and its high starting wages.The article closes with a suggestion that those who would turn away from oil and gas are just being influenced to damage the American energy sector.Fonzi is assertive in his beliefs, even in the face of commenters who passionately disagree with his opinions and despite the available research concluding otherwise.How can someone, let alone a journalist, be so willfully ignorant?Does the sharing of these attitudes have any real effect on society?Subjectivism states that there is always a myriad of factors at play as to why anything is the way that it is.Fonzi is a product of all the things he has experienced and has been influenced by all of the labels he has worn.According to the brief biography following the article, Fonzi is a veteran, having served in both Vietnam and Iraq, with 35 years of military intelligence under his belt.From the comments, it is suggested that he is an advocate of Donald Trump.He is also a journalist, one of the most popular tools used in the sharing of dominant moral codes (Philipzig, 2022).The comments accompanying Fonzi's article serve as a small example of the constant conflict occurring in society due to a sophisticated interplay of many different factors, which this paper will aim to explore as they apply to the current state of our world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.064 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".