Impact of Economic Recession on Adult Participation in Academic Programmes: The Case of Sandwich Programme of University Of Port Harcourt
Bibliographic record
Abstract
This paper examined the impact of economic recession on adult learners participation in academic programmes: the case of Sandwich Programme of University of Port Harcourt.The population for the study was 919 Year1 and Year2 adult learners in the Sandwich Programme.The sample size used was 551 participants which represents 60% of the entire population.Data were collected through questionnaire.The research questions were analysed using percentage and mean statistical tools while Pearson Product Moment Correlation Statistic was used to test the hypotheses at 0.05 alpha levels.The findings revealed that salary slash, non-payment of salaries, rise in price of goods and services, low economic activities are features of economic recession.Also many of those that were granted admission in 2015/2016 session did not turn up due to recession.It also revealed that Departmental students' enrolment was retrogressive in 2015/2016 in comparison to 2014/2015 session.The study further revealed that recession affects students' rate of attending lectures occasioned by late resumption, inadequacy of upkeeps, lack of transport fare for inter-campus shuttle and weakness resulting from underfeeding or non-feeding.Also acquisition of reading materials is affected by recession.Based on the above findings, recommendations made inter alia include that economic summits be convoked where professionals would proffer solution to recession; Each of the three tiers of government should make their budgets to be peopleoriented; diversify the economy; structures should be put in place to enhance upward social mobility vis-a-vis increased adult participation in academic programmes. Introduction:During the first quarter of 2016, some economists made some analysis of Nigerian economy and come up with the idea that it was titling negatively downwards.One of those with this view is Ekpo (2016: 42) who posits that "Nigerian economy is in a recession based on two consecutive negative GDP growth rates of -0.36 and -2.06% in the 1 st and 2 nd quarters of 2016".This negative trend of Nigerian economy became the order of the day as is found in the mass media.Unfortunately, this downturn in the economy was misconstrued as a criticism against the incumbent government.The government in their response repeatedly denied the existence of recession in the country.While the government turned deaf ears to these observations by economics analysts, the recessionary trend became more conspicuous to all and sundry as the prices of essential commodities were sky-rocketed.As the poor economic situation bit hard on majority of the Nigerian populace murmurings, became more audible.The government at this juncture admitted that the country is in recession.In support of the above assertion, Okoro (2016:9) stated that "after several months of denial, the government finally agreed that the Nigerian economy is in a period of recession".What then is recession?According to Hornby (2000:975) recession is "a difficult time for the economy of a country when there is less trade and industrial activity than usual and more people are unemployed".Also National Bureau of Economic research as (cited in John-Bray, 2016: 2-3) defined recession as "a significant decline in economic activity spread across the economy, lasting more than six months, normally visible in real Gross Domestic Product (GDP), including employment, industrial production, wholesale and retail sales".The two definitions above further confirm the claim that Nigeria is experiencing economic recession.It is no longer a hidden fact that the prices of essential goods like foodstuff such as rice, tomato, salt, garri, vegetable oil, palm oil, fish, meat, bread, beverages etc have gone so high that the average Nigerian can no longer afford.Similarly the prices of other goods and services have also gone up.Such goods as clothing (textile products), petroleum products, cosmetics as well as building materials are all very expensive.In the same vein, the costs of services like medicals (hospital bills) are relatively high.The situation has led many Nigerians to adjust their belts to the last possible point.No wonder the Nigerian Senate President, Senator Bukola Saraki (cited in Itua 2017:2) stated unequivocally that "Nigerians are facing widespread hunger, insecurity and inflation".This statement no doubt raises the profile of economic recession in Nigeria.In view of the above, the mass media is no longer dominated by whether there is recession or not rather solutions are being proffered to
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".