科學分支

出自維基百科,自由嘅百科全書
跳去導覽 跳去搵嘢

科學分枝英文branches of science)係指科學上對學科嘅分類。

自然科學[編輯]

內文:自然科學

自然科學(natural science)係泛指專門研究自然現象嘅科學領域[1]

  • 物理科學(physical science):研究啲原子或者係星球呢類冇生命嘅嘢。
    • 物理學(physics):研究宇宙嘅基本組成要素,好似係物質、物質喺時間空間入面嘅運動、以及好似係能量或者呢啲概念都係物理學嘅研究題目-細至構成物質嘅基本粒子,大至成個宇宙,都係物理學嘅研究範圍;物理學會透過分析同發現宇宙最基本嘅定律(物理定律)嚟去了解宇宙係點樣運作嘅,而且因為宇宙入面嘅嘢幾乎冚唪唥都會受制於呢柞定律,物理學可以話係自然科學之中最基本(fundamental),覆蓋面最廣嘅[2][3]
    • 化學(chemistry):研究物質同物質嘅特性,尤其係物質可以點樣變化;化學嘅範疇包括咗研究化學元素(原子嘅唔同款)以及係化合物(由若干隻元素結合而成嘅物質)嘅組成、結構同特性;除此之外,化學家仲成日研究物質點樣做化學反應-化學反應簡單講就係兩款或者以上嘅物質互動,分別噉變成第啲款嘅物質,仲有係化學反應發生嗰陣嘅能量變化(例如有啲化學反應發生嗰陣會釋放熱)等嘅問題[4][5]
    • 地球科學(earth science):專門研究地球嘅環境,涵括咗地質學(研究地球嘅岩石圈)、海洋學(研究)同氣象學(研究天氣氣候等)等嘅領域;呢啲研究好多時會用到物理學同化學上嘅知識,例如係研究地球嘅岩石圈嘅地質學就會試圖了解地殼嘅化學成份同埋由地核放出嚟嘅熱力點樣影響呢啲成份-前會涉及化學,而後者會涉及熱能壓力呢啲概念(物理學嘅範疇)[6][7]
    • 天文學(astronomy):研究天體,包括恆星行星衞星彗星星系呀噉;天文學會運用數學、物理學同化學嚟解釋宇宙入面唔同嘅天體點運作,簡單嘅例子包括咗用物理學上有關重力嘅概念同理論嚟分析啲行星點樣圍住恆星公轉(可以睇吓天體物理學[8][9]
  • 生命科學(life science):研究細菌植物等有生命嘅嘢。

... 等等。

社會科學[編輯]

內文:社會科學

社會科學(social science)係泛指研究社會文化層面嘅人類行爲嘅科學,社會科學都係跟住嗰套科學哲學同方法做研究嘅,堅持要用客觀同實證嚟理解人類。但因為社會科學研究嘅係人類,所以喺用嘅研究方法上就唔同啲,例如係社會科學冇得好似自然科學噉樣,自由噉操控佢哋嘅研究對象(人類),而且倫理上嘅限制比較多[19]-例如係生物學家大可以啲細菌種完之後就攞去銷毀,但係心理學家冇得將啲人做完研究就攞去銷毀,而且後者攞數據嗰陣又要考慮私隱嘅問題(原子或者細菌唔會同人要求私隱,但人就會),所以好多時冇辦法好似自然科學噉,話想攞咩數據就攞咩數據[20][21]

一般會俾人當做社會科學嘅領域包括咗:

... 等等。

除咗上述呢啲比較學術性嘅領域之外,屬於實用性嘅商學亦都有相當嘅社會科學成份,有陣時會俾人當做社會科學噉嚟睇:例子有管理學(management)研究人點樣聚埋一齊合作做嘢[29],或者係市場學(marketing)研究生意人要點做先能夠有效噉賣自己嘅產品[30]呀噉;呢啲領域都會涉及剖析人嘅行為(社會科學其中一樣最重要嘅嘢),包括分析打工仔工作表現會受啲咩因素影響(管理學常見課題)或者係研究消費者買嘢嘅意慾會受啲咩因素影響(市場學常見課題),而事實係,呢啲領域成日都會攞心理學上嘅概念用嚟分析人(打工仔或者消費者)嘅行為,同社會科學關係密切[31]

第啲分科[編輯]

內文:形式科學應用科學、 同 心靈科學
睇埋:硬同軟科學同埋精密科學

除咗自然學科同社會科學呢兩個大類之外,科學分科仲有以下呢啲分法:

  • 形式科學(formal science):研究形式系統,例如數學(mathematics)同邏輯學(logic)噉;呢柞靠演繹推理嚟追求知識嘅學科亦都同科學好有啦掕,幾乎所有科學家或多或少都會掂吓呢啲學科[32]。好似係數學噉,數學用演繹推理,主題研究數量結構空間,同頭先講咗嗰柞科學學科一樣咁嚴格;不過數學用嘅唔係歸納法,而係用證明嘅方法,只要自己嘅推論冇問題就會做得到確保自己得出嘅結論實係正確-所以數學喺追求知識嘅方法上明顯有異於物理學、生物學同絕大多數嘅社會科學,之但係科學又唔可以冇咗數學-數學成日引發科學上嘅重大突破,而且科學嘅過程裏面都無可避免要用到數學[33]。於是為咗分清楚,數學同埋邏輯呢柞學科俾學界嗌做「形式科學」,嚴格定義係指「研究形式語言嘅領域」[34][35]
  • 應用科學(applied science):有一啲學科會俾科學界嗌做「應用科學」-泛指一啲專係為咗將科學知識用喺實際用途上面而存在嘅學科,當中最出位嘅要數各門嘅工程學(engineering)同埋醫學(medicine);好似係醫學噉,醫學研究者會留意住化學界同生物學界有啲乜嘢新發現,用呢啲發現去發明新嘅醫療技術,再做實驗嚟睇吓呢啲新技術係咪真係得-即係用科學方法去探索「點樣將科學上嘅發現用喺實際用途嗰度」,所以俾人叫做「應用」科學[36][37];同應用科學相對嘅係所謂嘅純科學(pure science),即係指好似物理學、化學同心理學呢啲純粹係為咗追求知識而存在嘅科學領域。
  • 心靈科學(mind science):泛指研究心靈嘅科學領域-「研究心靈」包括咗研究人同第啲動物嘅情緒認知功能以至智能呀噉;心靈科學會研究神經系統(尤其係腦部)點樣透過各式各樣嘅過程表示、處理以及轉化資訊(神經科學同心理學),亦會諗埋心靈之間點樣用符號傳達資訊(語言學嘅範疇);心靈科學家會透過參考呢啲大自然所創造嘅心靈同心靈功能,嘗試創造出模擬心靈嘅電腦程式(即係所謂嘅人工智能;AI)。喺廿一世紀初,心靈科學包括咗心理學、神經科學、語言學同人工智能等嘅領域[38][39]
一個人腦由左面影嘅相;廿一世紀初心靈科學界一般相信心腦同一論,認為腦就係心靈嘅所在。
  • 硬科學(hard science)同軟科學(soft science):一般嚟講,如果一門採取科學方法嘅領域(科學)
    • 量度精確,而且
    • 客觀(例如唔使靠受試者口頭報告),
    • 就會俾人當係硬科學,否則就係軟科學[40]。舉幾個例說明,好似物理學同化學等嘅自然科學多數都會俾人當係硬科學;社會科學(除咗好數學性嘅經濟學之外)多數都會俾人當係軟科學,因為呢啲領域研究社會現象,好多時都會用問卷或者訪問等嘅方法要受試者答問題,用呢啲答覆做分析(數據嘅數字有可能受到受試者嘅主觀感覺影響)[41];而心理學就一半一半-研究腦活動研究外顯行為等唔使靠人類受試者口頭報告又唔靠文字性數據嘅心理學子領域一般俾人覺得係「硬」啲,相對嚟講社會心理學等比較依賴問卷同訪問嘅心理學子領域就俾好多人覺得係「軟」啲[42]
  • 跨學科(transdisciplinary):係指跨越傳統學科界限嘅研究[註 1];科學上嘅跨學科研究係會攞唔同領域上嘅理論同研究方法,嚟達致對研究對象有更加深入同全面嘅理解[43][44],例如喺廿世紀,研究人類點樣使錢俾人覺得係屬於經濟學嘅範疇,但現實表明咗,人使起錢上嚟好多時都係唔理性嘅,所以假設咗「決策者係完全理性」嘅舊派經濟模型搞唔掂,於是就有人諗點樣將心理學(涉及研究情緒等非理性嘅嘢)上嘅理論模型結合落去經濟學研究嗰度,成功噉解釋咗一啲廿世紀經濟學搞唔掂嘅現象,出咗行為經濟學呢個新嘅領域[45][46]

... 等等。

註釋[編輯]

  1. 原則上可以包埋人文等領域嘅跨學科研究。

睇埋[編輯]

[編輯]

  1. The Branches of Science.
  2. "Physics is one of the most fundamental of the sciences. Scientists of all disciplines use the ideas of physics, including chemists who study the structure of molecules, paleontologists who try to reconstruct how dinosaurs walked, and climatologists who study how human activities affect the atmosphere and oceans. Physics is also the foundation of all engineering and technology. No engineer could design a flat-screen TV, an interplanetary spacecraft, or even a better mousetrap without first understanding the basic laws of physics. (...) You will come to see physics as a towering achievement of the human intellect in its quest to understand our world and ourselves." Young & Freedman 2014, p. 1
  3. Lederman, L. M. (1984). The value of fundamental science. Scientific American, 251(5), 40-47.
  4. Brown, Theodore L.; LeMay, Jr., H. Eugene; Bursten, Bruce E.; Murphey, Catherine J.; Woodward, Patrick M.; Stoltzfus, Matthew W.; Lufaso, Michael W. (2018). "Introduction: Matter, energy, and measurement". Chemistry: The Central Science (14th ed.). New York: Pearson. pp. 46–85.
  5. "Chemistry Is Everywhere". American Chemical Society.
  6. What Is Earth Science?.
  7. Allaby M., 2008. Dictionary of Earth Sciences, Oxford University Press, ISBN 978-0-19-921194-4.
  8. Unsöld, A.; Baschek, B. (2001). The New Cosmos: An Introduction to Astronomy and Astrophysics. Springer.
  9. Carroll, B. W., & Ostlie, D. A. (2017). An introduction to modern astrophysics. Cambridge University Press.
  10. Trifonov, Edward N. (2012). "Definition of Life: Navigation through Uncertainties". Journal of Biomolecular Structure & Dynamics. Adenine Press. 29 (4): 647–50.
  11. "Molecular Biology". britannica.com.
  12. Griffiths, Anthony J.F.; Miller, Jeffrey H.; Suzuki, David T.; Lewontin, Richard C.; Gelbart, William M., eds. (2000). "Genetics and the Organism: Introduction". An Introduction to Genetic Analysis (7th ed.). New York: W. H. Freeman.
  13. Hartl, D, Jones, E (2005). Genetics: Analysis of Genes and Genomes (6th ed.). Jones & Bartlett.
  14. Mogilner, A., Wollman, R., & Marshall, W. F. (2006). Quantitative modeling in cell biology: what is it good for?. Developmental cell, 11(3), 279-287.
  15. Bear, MF; Connors, BW; Paradiso, MA (2007). "Ch. 2". Neuroscience: Exploring the Brain. Lippincott Williams & Wilkins.
  16. Bovet, É., Kraus, C., Panese, F., Pidoux, V., & Stücklin, N. (2013). Neuroscience examined by the clinical and the social science. Revue d'anthropologie des connaissances, 7(3), a-n.
  17. Hatcher, Bruce Gordon (1990). "Coral reef primary productivity. A hierarchy of pattern and process". Trends in Ecology and Evolution. 5 (5): 149-155.
  18. Chapin, F. Stuart; Pamela A. Matson; Harold A. Mooney (2002). Principles of Terrestrial Ecosystem Ecology. New York: Springer. p. 10.
  19. Fisher, C. B., & Anushko, A. E. (2008). Research ethics in social science. The SAGE handbook of social research methods, 95-109.
  20. Oberski, D. L., & Kreuter, F. (2020). Differential privacy and social science: An urgent puzzle. Harvard Data Science Review: HDSR, 2(1), 1-21.
  21. Colander, David C.; Hunt, Elgin F. (2019). "Social science and its methods". Social Science: An Introduction to the Study of Society (17th ed.). New York, NY: Routledge. pp. 1-22.
  22. Gluck, Mark A.; Mercado, Eduardo; Myers, Catherine E. (2016). Learning and Memory: From Brain to Behavior. New York/NY, USA: Worth Publishers.
  23. Cattell, R. B. (1988). Psychological theory and scientific method. In Handbook of multivariate experimental psychology (pp. 3-20). Springer, Boston, MA.
  24. Ashley, David; Orenstein, David M. (2005). Sociological Theory: Classical Statements (6 ed.). Boston: Pearson Education.
  25. Krugman, Paul; Wells, Robin (2012). Economics (3rd ed.). Worth Publishers.
  26. Halliday, Michael A.K.; Jonathan Webster (2006). On Language and Linguistics. Continuum International Publishing Group. p. vii.
  27. Haviland, William A.; Prins, Harald E.L.; McBride, Bunny; Walrath, Dana (2010), Cultural Anthropology: The Human Challenge (13th ed.), Cengage Learning,
  28. Stock, W.G., & Stock, M. (2013). Handbook of Information Science. Berlin, Boston, MA: De Gruyter Saur.
  29. Hersey, P., & Blanchard, K. H. (1969). Management of organizational behavior: Utilizing human resources. Academy of Management Journal, Vol. 12, No. 4.
  30. Lamb, Charles; Hair, Joseph; McDaniel, Carl (2016). Principles of Marketing. Boston, MA: Cengage Learning.
  31. Spector, P. E. (2021). Industrial and organizational psychology: Research and practice. John Wiley & Sons.
  32. Bill, Thompson (2007), "2.4 Formal Science and Applied Mathematics", The Nature of Statistical Evidence, Lecture Notes in Statistics, 189 (1st ed.), Springer, p. 15.
  33. Bishop, Alan (1997)."Mathematical Enculturation: A Cultural Perspective on Mathematics Education." Dordrecht: Kluwer Academic Publishers.
  34. "Formal Sciences". 原著喺2018年8月15號歸檔. 喺2017年8月3號搵到.
  35. "The Formal Sciences: Their Scope, Their Foundations, and Their Unity". Synthese. 133.
  36. Abraham, Reem Rachel (2004). "Clinically oriented physiology teaching: strategy for developing critical-thinking skills in undergraduate medical students". Advances in Physiology Education. 28 (3): 102–04.
  37. Bunge, M. (1966). "Technology as applied science". In Rapp, F. (ed.). Contributions to a Philosophy of Technology. Theory and Decision Library (An International Series in the Philosophy and Methodology of the Social and Behavioral Sciences). Dordrecht, Netherlands: Springer. pp. 19–39.
  38. Belkin, N. J. (1990). The cognitive viewpoint in information science. Journal of information science, 16(1), 11-15.
  39. Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional science, 32(1), 1-8.
  40. "In praise of soft science" (PDF). Nature. 435 (7045): 1003–2005. 2005.
  41. Smith L.D., Best L.A., Stubbs A., Johnston J., Archibald A.B. (2000). "Scientific Graphs and the Hierarchy of the Sciences". Social Studies of Science. 30 (1): 73–94.
  42. Smith, L. D., Best, L. A., Stubbs, D. A., Archibald, A. B., & Roberson-Nay, R. (2002). Constructing knowledge: The role of graphs and tables in hard and soft psychology. American Psychologist, 57(10), 749.
  43. Basarab Nicolescu (Ed.) Transdisciplinarity - Theory and Practice, Hampton Press, Cresskill, NJ, USA, 2008.
  44. Medicus, Gerhard (2005). "Mapping Transdisciplinarity in Human Sciences".; see also 2017: Being Human - Bridging the Gap between the Sciences of Body and Mind (chapter 1 and 2). Berlin, VWB,
  45. Lin, Tom C. W. (16 April 2012). "A Behavioral Framework for Securities Risk". Seattle University Law Review. SSRN.
  46. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American economic review, 93(5), 1449-1475.

[編輯]