Tourism Industry in Andhra Pradesh and Its Financial Performance – A Study of APTDC
Bibliographic record
Abstract
Tourism industry across the globe is one of the fast-growing industries starting from holiday pay package to medical tourism. It was in the last quarter of the 1900's that increased attention was paid to the desirability of holiday with pay. Up to the beginning of 20th century, tourists traveled almost exclusively by rail and steam ship. Since the beginning of 2000s medical tourism also became familiar as days passed on. In Andhra Pradesh, the Tourism Corporation was incorporated during the year 1976 in the name of Travel and Tourism Development Corporation Pvt. Ltd and became a wholly-owned Government company in March, 1980. In the year 1998, the Andhra Pradesh Tourism Development Corporation (APTDC) was a loss-making unit. There was a strong move to even close down the Corporation. In the year 2002–2005, APTDC generated revenues totaling Rs.330 million, making it one of the best State run corporations and now making a strong bid to be the top Tourism Corporation in India. In this paper, an attempt has been made to analyze the Financial Performance of APTDC, using selected financial ratios. The purpose of this paper is to assess the long-term efficiency of the corporation with which its activities are being managed as well as short-term performance in terms of its ability to meet current obligations. The study is carried out for a period of seven (07) years commencing from 2001–02 to 2008–09.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".