Presumption of Prefabricated Memes and Controllable Exploration of Meme Variations in the ESL Writing Teaching
Bibliographic record
Abstract
The semantic entities which are able to exist independently may serve as language memes according to the user’s intention. As language memes, the imitation or variation are influenced by human values and may be controlled and designed according to human objective. This paper proposes supposition of prefabricated writing meme, considering English as a second language(referred to as ESL) in the context of teaching writing, teacher as the authorities of meme spreading may construct with intended destination and spread the ESL writing memes with the characteristic of replication factor, which is constructed as the minimum unit of input, memory, storage and output. This paper also investigates the strategies of Attention, ‘i+1’ Input and Memeplexes to stabilize the variation of prefabricated writing memes and hence to avoid the negative impact of first language thinking in writing mode. Key words: ESL writing teaching; Meme; Imitation; Variation; Strategy
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".