Bibliographic record
Abstract
A growing concern for a sustainable environment has given rise to a rich corpus of fictional and non-fictional media products that address humans’ relationship with the natural world. Arguably, eco-films present narratives that submit as a convergence of physical and human environmental interactions. Jeta Amata’s filmic presentation, Black November is one of such. Black November presents the case of the Niger Delta region of Nigeria, addressing the dynamics of the ecological struggles and plights of the people. It goes further to present how a volatile and ravage community wages war against a perceived corrupt government and an international oil corporation in an attempt to save their community from being destroyed by frequent oil spills and excessive drilling. Interestingly, this creative depiction of non-fictional environmental challenges has manifested in an interactive driven medium as a video game that affords players an insight into the challenges and offered them a platform to decide how the challenges are addressed. The video game Niger Delta Commando is domiciled in Google play store; it brings to the knowledge of humanity the environmental degradation that defines the Niger Delta region and the resultant effect which includes the spring of militancy, combining also the effort initiated to curb activities of militants in the region. Niger Delta Commando opens up a relationship between the game participants and the Niger Delta environment, posing as an integral technological command between humans and ecology. Recognizing that interactivity allows for an alternative process to recreate, re-order and heighten significant incidents and experiences in order to draw attention, for remediation, this study seeks to reconcile the dominant trend in ecomedia and how the Nigerian narrative presents notions of interactive, immersive and environmental imperative that Niger Delta Commando evokes. Leaning on the relative theories of ecomedia and interactivity, this study adopts a dual methodology that is at once analytical and descriptive. It further maintains and appropriates a more detailed rendition and critical scrutiny that aligns the Niger Delta Commando with a more robust outlook. The attempt here is to objectify how this game appreciates interactive media that is unique and reads as a certain innovation in the Nigerian environment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".