Bibliographic record
Abstract
Musa Idris Okpanachi, an Associate Professor of English at the Department of English, Federal University, Dutse, Jigawa state, has authored three collections of poems: The Eaters of the Living (2007) , From the Margins of Paradise (2012), and Music of the Dead (2016). Dark, haunting images of blood, corpses, and cemetery recur in his poetry, apparently depicting the regularity of death in his country. By utilizing satiric and hyperbolic elements, limpid diction interspersed with sepulchral images, Okpanachi uncovers the degeneration usually indicative of postcolonial failure. It is this persistent degeneration in Nigeria that animates Okpanachi's preoccupation with funereal imagery. His latest poetry collection, Music of the Dead , is made up of 72 poems. It opens with ‘A Long Silence’, a prose poem which presents in dense rhythm a montage of uncanny scenes of ‘an age stranger than time and chameleon’ (1). In this strange age, the news of people dying is so common, but the causes of their deaths might seem ludicrous were it not strange. Due to the commonness of death in the land, the poet laments that ‘the graveyards are full; the country is a cemetery in the hands of the dead’ (2). The poem prefaces the kind of grim imagery one will mainly come across in the collection – imagery that induces anything but hope and cheer. The sequence of poems in ‘Dogs and Angels 1-111’ narrate the manifestations of ‘gallows’, ‘nooses’, ‘graves’, ‘houses ablaze’, ‘cemetery’ etc., (9-12). ‘The Forerunner’ portrays the ruler's perversity which leaves behind a trail of death (18), while the poem ‘Sharks’ is shot through with grisly images: Death is a million Magic numbers In democratic coffins We have dug 150 million graves. (26) The theme of death finds more resonance in ‘Black Flower’, a poem recounting the ‘seasons of massacre’ (28). In ‘The Hawkers of Blood’, the poet comments on the sense of vile triumphalism exhibited by killers who boast that they are heirs of the ‘primitive gods of war’ and delight ‘in celebration of death’ (76). The scenes depicted in this poem evoke the devastation wrought in certain parts of Nigeria by Boko Haram, an Islamist terrorist group.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".