Revisioning History: A Deconstructionist Reading of a Learner’s Multimodal Text, Revenge
Bibliographic record
Abstract
Recent interest in multimodality recognizes the integration of text and image in meaning-making as representing reality. It has also been argued that with the use of digital communication, the meanings of visual and verbal data can be easily manipulated rendering them unreliable. As such, a close and critical reading of the text is required to discover what is hidden, absent, or inconsistent with it. In a deconstruction of a multimodal digital composition of a poem that involves revisioning of history, this paper privileges the absences of cultural and historical texts to signify socio-political issues. An eclectic use of theoretical concepts on meaning-making, especially those proposed by Kress and van Leeuwen, Foucault and Baudrillard, constructs the discussion of the analysis. The digital poem entitled ‘Revenge’ is deconstructed to further discover such absence in the text. The findings reveal that language and images are used by the learner as a source of power to negotiate the boundaries of identity. It has also been discovered that the message in rhetoric and visuals complement each other to support the process of meaning-making.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".