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Record W1491049421 · doi:10.5539/hes.v5n4p56

Survey on the Assessment of the Current Actual Expenses Incurred by Students on the Meals and Accommodation within and around the Campuses: The Case of Tanzania Higher Education Students’ Loans Beneficiaries

2015· article· en· W1491049421 on OpenAlexvenueno aff
Veronica R. Nyahende, Asangye N. Bangu, Benedicto C. Chakaza

Bibliographic record

VenueHigher Education Studies · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicBusiness Strategies and Management Research
Canadian institutionsnot available
FundersUniversity of Dar es Salaam
KeywordsTanzaniaAllowance (engineering)AccommodationCost of livingHigher educationBusinessMarketingSocioeconomicsEconomic growthEconomicsPsychologyOperations management

Abstract

fetched live from OpenAlex

This Survey analyses the current actual expenses incurred by students on the meals and accommodation within and around the campuses. The study was geared towards achieving the following objectives: (i) to examine the current cost incurred by a students for meals In Campus, (ii) to examine the current cost incurred by a students for accommodation In Campus, (iii) to examine the current cost incurred by a students for meals Off Campus, (iv) to examine the current cost incurred by a students for accommodation Off Campus, (v) to identify the Institutional indicated Prices for both Meals and Accommodations There have been many complaints from various stakeholders concerning the current meals and accommodation allowance given, which was last revised in year 2010/2011. The allowances given was claimed to be low compared to the real cost of living which is very much affected by inflation. The fact was also supported by the Parliament during 2014/2015 budget session. Based on these complains, and the real market situation it becomes necessary for Higher Education Students’ Loans Board (HESLB) to conduct this survey. The survey was conducted in 13 regions in Tanzania, Dar es salaam, Arusha, Kilimanjaro, Tanga, Mtwara, Morogoro, Iringa, Mbeya, Dodoma, Tabora, Mwanza, Kagera, and Unguja, in which 70 universities and 105 cafeteria were visited. Data were collected from 1120 students’ respondents and 105 managers/owners of the cafeterias/hotels/kiosks. Data were analysed using SPSS computer software. The study concluded that students are willing to pay Tanzanian Shillings (Tshs.) 5,000 at minimum for breakfast, lunch and dinner and also a maximum of Tshs. 7,500. It was further shown that students prefer paying accommodation for both In Campus and Off Campus at the rate between Tshs. 300 and Tshs. 700 even though they actually pay between Tshs. 1,000 and Tshs. 1,700 Off Campus and between Tshs. 300 and Tshs. 1,000 for accommodation In Campus. Also 90% of students’ respondents revealed that Tshs. 7,500 given now as meals and accommodation allowance is not sufficient. It was also concluded that the institutional set prices for meals and accommodation have no any effect on the real price prevailing. Based on the results of the analysis the study recommended that HESLB should advise the Government to consider revising the allowances for meals (breakfast, lunch and dinner) at the students maximum preference which is Tshs. 7,500 plus accommodation cost which should be at Tshs. 2,500 (a maximum amount paid for accommodation Off Campus + associated costs such as water bills, electricity bills and security bills) the sum should be equal to Tshs. 10,000 which was the maximum amount preferred by more than 50% of the students’ respondents, that is Tshs. (7,500 + 1,700 + 800 = 10,000). Universities should administer and manage the cafeterias within the university so that they can control prices, quality as well as taste and preference of the students because these cafeterias can save a lot of students as they spend most of their time In Campus.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.341
GPT teacher head0.520
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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