Empowerment Through Knowledge of Accounting and Related Disciplines: Participatory Action Research in an African Village
Bibliographic record
Abstract
ABSTRACT Accounting scholars are challenged to discover ways to facilitate a broader engagement with the oppressed and poor toward a more just and fair world. This paper reports an interaction between an accounting educator and disadvantaged Kenyan villagers in an exploratory attempt to expand the reach of critical accounting research from the confines of academia to practice. In Africa, the end of colonialism left widespread poverty that was exacerbated by illiteracy and ignorance. At the same time, the World Bank and the International Monetary Fund (IMF) required newly independent African states to implement neo-liberal-inspired policies that weakened state social governance. This, in turn, led to the growth of religious and non-governmental organizations (NGOs) whose policies aimed to fill the gaps in government social services that alleviate inequities. Ignorance enslaves, but knowledge—including knowledge of accounting and financial systems—will empower the poor to evaluate the motives, desirability, and achievements of governmental and NGO services and programs introduced to ease poverty. The specific aim of this modest, grassroots intervention was to share financial knowledge with members of a church in Bungoma, a poor region in Northwestern Kenya. This participatory action research (PAR) intervention was carefully implemented to respect the values and culture of the village participants, and avoided Western values and praxis to maintain the villagers' status quo. Instead, the accounting educator introduced empathetic learning by relating accounting principles to the Christian values of the villagers. The paper concludes with a discussion on the outcomes and limitations of this intervention.
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".