Going to the Bittersweet Roots vs. New World Blues in Gurnah’s Gravel Heart- A Psychological Analysis
Bibliographic record
Abstract
This descriptive qualitative paper aims at presenting a close reading of the protagonist Salim’s reminiscences in Abdulrazak Gurnah’s Gravel Heart in terms of an eclectic approach toward representation of trauma. Psychological trauma refers to the unbearable, untreatable, and unspeakable psychological wounds remaining on the subject’s unconsciousness. The most widely used method for studying trauma is based on Freud's, (1995) psychoanalytic study. Alongside with Freud’s theory, Kristal- Andersson’s(2000) and Felititi & etal.,’s (1998) studies are also used to draw the descriptions and explanations of trauma in Sali, the protagonist of Gravel Heart. Also, this study is a clarion call to the authorities at the top to provide a realistic technique and manner of working with immigrants in counseling and provide able and humanitarian assistance- services as the study also found out like many other real heroes, Salim a victim of bitter childhood and as an immigrant.Abdul Razak Gurnah,who was honoured with the Nobel Prize for Literature (2021) for his true and passionate retelling of the woes, longings of immigrants in general, and specifically Africans. in Gravel Heart, one among his evocative oeuvre retells the ties that bind’ as well as ‘the ties that fray.’ (Telegraph) Hence this study would pave way to the emergence of likewise multidisciplinary studies to blend psychological frameworks to investigate the causes of real as well as fictional character’s trauma.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".