Bibliographic record
Abstract
The current paper attempts to propose a device by which ideologically based responses can be measured in the interpersonal discourse of ideology. As a result of some drastic political changes, the Egyptian society has witnessed during the last five years a wave of conflicting ideologies, each of which is endorsed by some people and rejected by others. The different types of social media such as Facebook and Twitter are global and influential channels of the communication of diverse ideologies. A Facebook account of a supposedly Egyptian Jewish woman was specially created for collecting the data to be analysed in this paper. The paper is a qualitative research whose data is collected through an interaction between a Facebook account owner and some other users. The data is categorized into three groups each of which represents a certain area in a spectrum of ideologically based responses. One of the main findings reached through data analysis is that ideological responses can be measured and that there are mainly three types of responses in interpersonal ideological discourse, namely positive, passive and negative. It also concludes that each type of responses displays some distinctive features. This spectrum helps measure and interprets different types of responses to one’s ideology.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".