POSTHUMANIST APPLIED LINGUISTICS: A TRANSPARENT ASSIGNMENT FOR WRITING RESEARCH PROPOSAL ABSTRACTS
Bibliographic record
Abstract
Students had limited knowledge of posthumanism in applied linguistics. The assignment aimed to deepen their understanding while advancing academic and research skills. In the context of posthumanist thought, AI and chatbots are making digital technology increasingly essential in foreign language use and instruction. In posthumanist thought, machines are integral enhancements that merge with human cognition, expanding linguistic processing, multilingual interaction, and knowledge production. Pennycook’s concept of Posthumanist Applied Linguistics builds on this notion, challenging us to rethink cognition, language learning, and the interdependence of humans with nonhuman and technological entities. This study aims to discover whether doctoral students are prepared to conduct research within this emerging framework of applied linguistics. As part of the IKU 7 initiative, nineteen doctoral students enrolled in an advanced applied linguistics course were tasked with exploring this new perspective. They were guided through a transparent assignment design to develop research proposal abstracts. The design, rooted in inclusive pedagogy, ensures all students can learn by providing suitable conditions tailored to their unique needs. Quantitative analysis of the submitted abstracts revealed that students struggled to identify suitable research topics within this novel framework, due to a lack of practical knowledge in research methodology and limited understanding of posthumanist principles. Consequently, many students produced unclear titles and abstracts, with 11 out of 19 (over half) omitting the methodology section
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".