Vicissitudes of Aphasic Identity: Discourse Analysis Under James Paul Gee’s Identity Framework
Bibliographic record
Abstract
Patients with post-stroke aphasia experience inability to communicate fluently, which is associated with an injury in the language areas of the brain. While much literature is available on the impact of aphasia suffered by the patient on family and important others, there is a dearth of data concerning the aspects of identity construction of the patient after the disastrous consequences of aphasia disorder. A discourse analytical framework was used by employing James Paul Gee’s framework of identity perception with the aim of understanding the vicissitudes of identity in patients with aphasia. Data were obtained from semi-structured interviews of three participants and their partners. The interviews were video-recorded, transcribed and analysed using Paul Gee’s Toolkit of doing discourse analysis; including four perspectives of identity driven by nature, institution, discourse and affinity. All these aspects were recognised as a negative construction of identity after aphasia disorder except some instances of positive construction in Affinity-Identity. The study concluded that post-stroke reconstruction of identity was an important challenge for the patients, family and healthcare services. In most of the cases, this reconstruction was negatively managed by the patients with aphasia and people surrounding them. Therefore, the present study has suggested the need to develop physical and virtual aphasia groups, such as aphasia clubs, aphasia tea houses and Facebook/WhatsApp aphasia groups, so that patients with aphasia can construct a positive Affinity-Identity within their affinity groups and general identity in other aspects of life. Moreover, a sound and effective training is recommended at social level to sensitize people about patients with special needs.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".